Multi-phase consolidation optimization tool

ABSTRACT

Rule data sets are received. These rule sets are associated with constraints controlling how records that are associated with the goods are consolidated. These goods are to be received for importing. An estimate score indicative of the risk for inspection for a first set of goods that are to be imported is generated. Based at least in part on the rule data sets and the generated estimate, a plurality of records are consolidated to a single instance for the first set of goods. Based on the consolidating, a user interface is caused to be generated that renders information associated with the consolidating.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation to U.S. Non provisional patentapplication Ser. No. 16/992,311, filed Aug. 13, 2020, which claimspriority to U.S. Provisional Patent Application No. 62/886,154 entitled“MULTI-PHASE CONSOLIDATION OPTIMIZATION TOOL,” filed Aug. 13, 2019, thecontents of which is incorporated by reference in its entirety.

TECHNICAL FIELD

The present disclosure generally relates to operator interfaces andspecific optimization computer applications. In particular, thisdisclosure relates to user interfaces or optimization logic, such asmachine learning software tools associated with consolidation.

BACKGROUND

Typical software applications and other technologies store data andstatically generate functions to help users make cost-based decisions.For example, if a user wanted to calculate the cost of importingfreight, the user may use electronic spreadsheets or other tools tomanually input data, after which these applications perform simplelinear calculations to give users a sense of overall costs. In anillustrative example, to calculate a Merchandise Processing Fee (MPF),an electronic spreadsheet application may receive user input of theoverall value of a shipment or freight in a logical field. Theelectronic spreadsheet application may then perform a simplemultiplication function by 0.3464 to determine the MPF fee. This givesusers a sense of the MPF fee that will be due for importing the freight.However, these applications and other technologies do not dynamicallylearn or predict behavior based on unique signals or rules (e.g., riskof inspection, learned patterns from customs agencies or other entities,etc.), among other things.

BRIEF DESCRIPTION OF THE DRAWINGS

Having thus described the disclosure in general terms, reference willnow be made to the accompanying drawings, which are not necessarilydrawn to scale, and wherein:

FIG. 1 is a schematic diagram of an example computing environment inwhich aspects of the present disclosure are employed in, according tosome embodiments.

FIG. 2 is a schematic diagram of one or more logistics server(s) inwhich aspects of the present disclosure are employed in, according tosome embodiments.

FIG. 3 is a schematic diagram of a computing entity in which aspects ofthe present disclosure are employed in, according to some embodiments.

FIG. 4 is a block diagram of the logistics vehicle of FIG. 1 , accordingto some embodiments.

FIG. 5 is a block diagram of an example system architecture, in whichsome embodiments of the present disclosure are employed in.

FIG. 6 is a screenshot of a user interface indicating particular vesselsthat have been optimized for consolidation, according to someembodiments.

FIG. 7 is a screenshot of an example user interface illustratingoptimization details, according to some embodiments.

FIG. 8 is a screenshot of an example user interface illustratingpurchase orders that are consolidated to particular entries and how theuser can change or override optimization results via the user interface,according to some embodiments.

FIG. 9 is a screenshot of an example user interface that illustrates ahistorical view that tracks cumulative savings over a period of time,after optimization has occurred, according to some embodiments.

FIG. 10 is a screenshot of an example user interface that illustratescosts associated with consolidating and not consolidating purchaseorders, according to some embodiments.

FIG. 11 is a screenshot of an example use interface that maps costs andsavings with various entries, according to some embodiments.

FIG. 12 is a screenshot of an example user interface that illustratesspecific purchase order information for a given entry, according to someembodiments.

FIG. 13 is a screenshot of an example user interface illustratinginvoice information of a particular purchase order, according to someembodiments.

FIG. 14 is a flow diagram of an example process for consolidatingrecords and generating a user interface, according to some embodiments.

FIG. 15 is a schematic diagram of an example visualization of featurespace that illustrates various feature vectors representing individualgoods (or purchase orders that include a group of goods) andcorresponding risk-of-inspection groups, according to some embodiments.

DETAILED DESCRIPTION OF THE INVENTION

The present disclosure will now be described more fully hereinafter withreference to the accompanying drawings, in which some, but not allembodiments of the disclosure are shown. Indeed, the disclosure may beembodied in many different forms and should not be construed as limitedto the embodiments set forth herein. Rather, these embodiments areprovided so that this disclosure will satisfy applicable legalrequirements. Like numbers refer to like elements throughout.

I. OVERVIEW

As described above, existing technologies are static in that they aredesigned to receive extensive manual input after which they performsimple calculations. For example, certain spreadsheet computing toolsare designed to receive user input in various fields and perform severalcalculations, such as trigonometry functions (e.g., return cosign of agiven angle) or the chi-squared statistical test for independence. If auser wanted to, for example, consolidate freight records (e.g., purchaseorders) to particular import entries, the user would have to manuallyinput various fields of information, such as value of goods,multiplication factor, port, etc., after which the tool would performstatic calculations to generate cost. The user may then manually decideto consolidate records to entries based on the tool cost output.However, these tools provide no way to optimize consolidation based onseveral new rules or factors, some of which are learned. Other tools,particularly in the freight industry, have user interfaces that arenon-intuitive and require extensive drilling down of several layersbefore information is found.

Various embodiments of the present disclosure improve these existingtechnologies because they include new improved functionality notemployed by these existing technologies. For example, variousembodiments are directed to an optimization tool that automaticallyconsolidates records based on various factors, signals, or phases (e.g.,risk of inspection, learned customs agency behavior, PO buyer rules, POseller rules, etc.). Further, particular embodiments improve existingsoftware technologies by automating tasks (e.g., record consolidation)via certain new rules (e.g., risk of inspection threshold, customsrules, buyer or seller rules, etc.) that existing technologies do notutilize. As described above, such tasks are not automated in variousexisting technologies and have only been historically performed byhumans or manual input of users using simplistic linear functionality.In particular embodiments, incorporating these certain new rules improveexisting technological processes by allowing the automation of thesecertain tasks. Some embodiments of the present disclosure improveexisting technologies by providing intuitive user interfaces that do notrequire extensive drilling down and tedious action. Accordingly, userscan efficiently navigate these user interfaces

Various existing technologies also consume an unnecessary quantity ofcomputing resources, such as memory. For example, particulartechnologies do not consolidate purchase orders to particular entries.Each entry, however, may be allocated a memory address. Some embodimentsof the present disclosure consolidate records based on several factorsor rules, such as the probability of inspection and various learnedinsights. Because several records are consolidated as a single entry,memory utilization is decreased because the other single records are notstored as separate entries. For instance, the consolidated single entrycan be assigned a single logical pointer, handle, or other referenceindicator (as opposed to multiple reference indicators referencingindividual records in existing technologies) such that all of therecords can be located in memory in a single I/O (e.g., a read). Thiscan improve CPU execution, for example, by reducing the amount of lookuptime in memory to locate entries. Specifically, for example, CPU fetch,decode, and or execute functions can be improved, such as by decreasingfetch time by reducing the amount of time it has to take an addressnumber associated with an instruction (e.g., an entry) from a programcounter stored in memory since records are consolidated. Additionally,record or entry consolidation can reduce storage device (e.g., disk) I/O(e.g., excess physical read/write head movements on non-volatile disk)because consolidated records can be accessed in memory in a single passor single I/O operation, as opposed to multiple, so requests do not haveto repetitively reach out to the storage device to perform read/writeoperations. This can be important since repetitive I/O operations caneventually wear on components, such as a read/write head increase thelikelihood of I/O errors (e.g., the read/write head reads an incorrectsector).

Additionally, fewer queries may be needed to obtain or access eachrecord. Existing technologies issue repetitive queries to obtain eachrecord or entry but this is computationally expensive. For example, anoptimizer engine of a database manager module calculates a queryexecution plan (e.g., calculates cardinality, selectivity, etc.) eachtime a query is issued, which requires a database manager to find theleast expensive query execution plan to fully execute the query. Mostdatabase relations, such as tables, contain hundreds if not thousands ofrecords. Repetitively calculating query execution plans to obtain eachrecord or entry, decreases throughput and increases network latency.However, because certain embodiments consolidate entries, when anoptimizer engine of a database manager module calculates a queryexecution plan (e.g., calculates cardinality, selectivity, etc.) for theconsolidated entries, the database manager only has to find the leastexpensive query execution plan for one record (i.e., the consolidatedentry), instead of multiple records, which increases throughput anddecreases network latency.

II. APPARATUSES, METHODS, AND SYSTEMS

Embodiments of the present disclosure may be implemented in variousways, including as apparatuses that comprise articles of manufacture. Anapparatus may include a non-transitory computer-readable storage mediumstoring applications, programs, program modules, scripts, source code,program code, object code, byte code, compiled code, interpreted code,machine code, executable instructions, and/or the like (also referred toherein as executable instructions, instructions for execution, programcode, and/or similar terms used herein interchangeably). Suchnon-transitory computer-readable storage media include allcomputer-readable media (including volatile and non-volatile media).

In one embodiment, a non-volatile computer-readable storage medium mayinclude a floppy disk, flexible disk, hard disk, solid-state storage(SSS) (e.g., a solid state drive (SSD), solid state card (SSC), solidstate module (SSM)), enterprise flash drive, magnetic tape, or any othernon-transitory magnetic medium, and/or the like. A non-volatilecomputer-readable storage medium may also include a punch card, papertape, optical mark sheet (or any other physical medium with patterns ofholes or other optically recognizable indicia), compact disc read onlymemory (CD-ROM), compact disc-rewritable (CD-RW), digital versatile disc(DVD), Blu-ray disc (BD), any other non-transitory optical medium,and/or the like. Such a non-volatile computer-readable storage mediummay also include read-only memory (ROM), programmable read-only memory(PROM), erasable programmable read-only memory (EPROM), electricallyerasable programmable read-only memory (EEPROM), flash memory (e.g.,Serial, NAND, NOR, and/or the like), multimedia memory cards (MMC),secure digital (SD) memory cards, SmartMedia cards, CompactFlash (CF)cards, Memory Sticks, and/or the like. Further, a non-volatilecomputer-readable storage medium may also include conductive-bridgingrandom access memory (CBRAM), phase-change random access memory (PRAM),ferroelectric random-access memory (FeRAM), non-volatile random-accessmemory (NVRAM), magnetoresistive random-access memory (MRAM), resistiverandom-access memory (RRAM), Silicon-Oxide-Nitride-Oxide-Silicon memory(SONOS), floating junction gate random access memory (FJG RAM),Millipede memory, racetrack memory, and/or the like.

In one embodiment, a volatile computer-readable storage medium mayinclude random access memory (RAM), dynamic random access memory (DRAM),static random access memory (SRAM), fast page mode dynamic random accessmemory (FPM DRAM), extended data-out dynamic random access memory (EDODRAM), synchronous dynamic random access memory (SDRAM), doubleinformation/data rate synchronous dynamic random access memory (DDRSDRAM), double information/data rate type two synchronous dynamic randomaccess memory (DDR2 SDRAM), double information/data rate type threesynchronous dynamic random access memory (DDR3 SDRAM), Rambus dynamicrandom access memory (RDRAM), Twin Transistor RAM (TTRAM), Thyristor RAM(T-RAM), Zero-capacitor (Z-RAM), Rambus in-line memory module (RIMM),dual in-line memory module (DIMM), single in-line memory module (SIMM),video random access memory (VRAM), cache memory (including variouslevels), flash memory, register memory, and/or the like. It will beappreciated that where embodiments are described to use acomputer-readable storage medium, other types of computer-readablestorage media may be substituted for or used in addition to thecomputer-readable storage media described above.

As should be appreciated, various embodiments of the present disclosuremay also be implemented as methods, apparatus, systems, computingdevices/entities, computing entities, and/or the like. As such,embodiments of the present disclosure may take the form of an apparatus,system, computing device, computing entity, and/or the like executinginstructions stored on a computer-readable storage medium to performcertain steps or operations. However, embodiments of the presentdisclosure may also take the form of an entirely hardware embodimentperforming certain steps or operations.

Embodiments of the present disclosure are described below with referenceto block diagrams and flowchart illustrations. Thus, it should beunderstood that each block of the block diagrams and flowchartillustrations may be implemented in the form of a computer programproduct, an entirely hardware embodiment, a combination of hardware andcomputer program products, and/or apparatus, systems, computingdevices/entities, computing entities, and/or the like carrying outinstructions, operations, steps, and similar words used interchangeably(e.g., the executable instructions, instructions for execution, programcode, and/or the like) on a computer-readable storage medium forexecution. For example, retrieval, loading, and execution of code may beperformed sequentially such that one instruction is retrieved, loaded,and executed at a time. In some exemplary embodiments, retrieval,loading, and/or execution may be performed in parallel such thatmultiple instructions are retrieved, loaded, and/or executed together.Thus, such embodiments can produce specifically-configured machinesperforming the steps or operations specified in the block diagrams andflowchart illustrations. Accordingly, the block diagrams and flowchartillustrations support various combinations of embodiments for performingthe specified instructions, operations, or steps.

III. EXEMPLARY SYSTEM ARCHITECTURE

FIG. 1 is a schematic diagram of an example computing environment 100 inwhich aspects of the present disclosure are employed in, according tosome embodiments. As shown in FIG. 1 , this particular computingenvironment 100 includes one or more freight vessels 120, one or morelogistics servers 105, one or more computing entities 110 (e.g., amobile device, such as a DIAD), one or more satellites 112, one or morenetworks 135, a customs data store 123, and a buyer/seller data store125. Each of these components, entities, devices, systems, and similarwords used herein interchangeably may be in direct or indirectcommunication with, for example, one another over the same or differentwired and/or wireless networks. Additionally, while FIG. 1 illustratesthe various system entities as separate, standalone entities, thevarious embodiments are not limited to this particular architecture.

In various embodiments, the network(s) 135 represents or includes an IoTnetwork, which is a network of interconnected items that are eachprovided with unique identifiers (e.g., UIDs) and computing logic so asto communicate or transfer data with each other or other components.Such communication can happen without requiring human-to-human orhuman-to-computer interaction. For example, an IoT network may includethe freight vessel 120, which is equipped with one or more sensors andtransmitter in order to process and/or transmit sensor data over thenetwork 135 to the logistics server(s) 105. In the context of an IoTnetwork, a computer (not shown) within the freight vessel can be orinclude one or more local processing devices (e.g., edge nodes) that areone or more computing devices configured to store and process, over thenetwork(s) 135, either a subset or all of the received or respectivesets of data to the one or more remote computing devices (e.g., thecomputing entities 110 and/or the logistics server(s) 105) for analysis.

In some embodiments, the local processing device(s) is a mesh or othernetwork of microdata centers or edge nodes that process and store localdata received from sensors coupled to the freight vessel 120 and push ortransmit some or all of the data to a cloud device or a corporate datacenter that is or is included in the one or more logistics server(s)105. In some embodiments, the local processing device(s) store all ofthe data and only transmit selected (e.g., data that meets a threshold)or important data to the one or more logistics servers 105. Accordingly,the non-important data or the data that is in a group that does not meeta threshold is not transmitted. For example, a lidar, radar, and/orcamera sensor located within the freight vessel 120 may sample map databut only push a portion of the map data. Accordingly, only after thecondition or threshold has been met, do the local processing device(s)transmit the data that meets or exceeds the threshold to remotecomputing devices such that the remote device(s) can take responsiveactions, such as notify a user mobile device (e.g., computing entity110) indicating the threshold has been met and/or cause a modificationof data (e.g., consolidate entries of purchase orders). The data thatdoes not meet or exceed the threshold is not transmitted in particularembodiments. In various embodiments where the threshold or condition isnot met, daily or other time period reports are periodically generatedand transmitted from the local processing device(s) to the remotedevice(s) indicating all the data readings gathered and processed at thelocal processing device(s). In some embodiments, the one or more localprocessing devices act as a buffer or gateway between the network(s) anda broader network, such as the one or more networks 135. Accordingly, inthese embodiments, the one or more local processing devices can beassociated with one or more gateway devices that translate proprietarycommunication protocols into other protocols, such as internetprotocols.

In some embodiments, the logistics server(s) 105 generates or causesuser interfaces (e.g., FIGS. 6 through 13 ) to be displayed (e.g., tothe computing entities 110) and/or consolidates entries based on one ormore factors, such as rules indicated in the customs data store 123and/or the buyer/seller data store 125 and/or sensor data detected fromsensors within the freight vessel 120, as described in more detailbelow. Alternatively or additionally, in some embodiments, the logisticsserver(s) 105 cause a control signal to be transmitted based on one ormore factors, such as rules indicated in the customer data store 123and/or the buyer/seller data store 123 and/or sensor data detected fromsensors within the freight vessel, as described in more detail below.

1. Exemplary Analysis Computing Entities

FIG. 2 provides a schematic of a logistics server(s) 105 according toparticular embodiments of the present disclosure. In general, the termscomputing entity, computer, entity, device, system, and/or similar wordsused herein interchangeably may refer to, for example, one or morecomputers, computing entities, desktops, mobile phones, tablets,phablets, notebooks, laptops, distributed systems, consoles inputterminals, servers or server networks, blades, gateways, switches,processing devices, processing entities, set-top boxes, relays, routers,network access points, base stations, the like, and/or any combinationof devices or entities adapted to perform the functions, operations,and/or processes described herein. Such functions, operations, and/orprocesses may include, for example, transmitting, receiving, operatingon, processing, displaying, storing, determining, creating/generating,monitoring, evaluating, comparing, and/or similar terms used hereininterchangeably. In particular embodiments, these functions, operations,and/or processes can be performed on data, content, information/data,and/or similar terms used herein interchangeably.

As indicated, in particular embodiments, the logistics server(s) 105 mayalso include one or more communications interfaces 220 for communicatingwith various computing entities, such as by communicating data, content,information/data, and/or similar terms used herein interchangeably thatcan be transmitted, received, operated on, processed, displayed, stored,and/or the like.

As shown in FIG. 2 , in particular embodiments, the logistics server(s)105 may include or be in communication with one or more processingelements 205 (also referred to as processors, processing circuitry,and/or similar terms used herein interchangeably) that communicate withother elements within the logistics server(s) 105 via a bus, forexample. As will be understood, the processing element 205 may beembodied in a number of different ways. For example, the processingelement 205 may be embodied as one or more complex programmable logicdevices (CPLDs), microprocessors, multi-core processors, co-processingentities, application-specific instruction-set processors (ASIPs),microcontrollers, and/or controllers. Further, the processing element205 may be embodied as one or more other processing devices orcircuitry. The term circuitry may refer to an entirely hardwareembodiment or a combination of hardware and computer program products.Thus, the processing element 205 may be embodied as integrated circuits,application specific integrated circuits (ASICs), field programmablegate arrays (FPGAs), programmable logic arrays (PLAs), hardwareaccelerators, other circuitry, and/or the like. As will therefore beunderstood, the processing element 205 may be configured for aparticular use or configured to execute instructions stored in volatileor non-volatile media or otherwise accessible to the processing element205. As such, whether configured by hardware or computer programproducts, or by a combination thereof, the processing element 205 may becapable of performing steps or operations according to embodiments ofthe present disclosure when configured accordingly.

In particular embodiments, the logistics server(s) 105 may furtherinclude or be in communication with non-volatile media (also referred toas non-volatile storage, memory, memory storage, memory circuitry and/orsimilar terms used herein interchangeably). In particular embodiments,the non-volatile storage or memory may include one or more non-volatilestorage or memory media 210, including but not limited to hard disks,ROM, PROM, EPROM, EEPROM, flash memory, MMCs, SD memory cards, MemorySticks, CBRAM, PRAM, FeRAM, NVRAM, MRAM, RRAM, SONOS, FJG RAM, Millipedememory, racetrack memory, and/or the like. As will be recognized, thenon-volatile storage or memory media may store databases (e.g.,parcel/item/shipment database), database instances, database managementsystems, data, applications, programs, program modules, scripts, sourcecode, object code, byte code, compiled code, interpreted code, machinecode, executable instructions, and/or the like. The term database,database instance, database management system, and/or similar terms usedherein interchangeably may refer to a collection of records orinformation/data that is stored in a computer-readable storage mediumusing one or more database models, such as a hierarchical databasemodel, network model, relational model, entity—relationship model,object model, document model, semantic model, graph model, and/or thelike.

In particular embodiments, the logistics server(s) 105 may furtherinclude or be in communication with volatile media (also referred to asvolatile storage, memory, memory storage, memory circuitry and/orsimilar terms used herein interchangeably). In particular embodiments,the volatile storage or memory may also include one or more volatilestorage or memory media 215, including but not limited to RAM, DRAM,SRAM, FPM DRAM, EDO DRAM, SDRAM, DDR SDRAM, DDR2 SDRAM, DDR3 SDRAM,RDRAM, TTRAM, T-RAM, Z-RAM, RIMM, DIMM, SIMM, VRAM, cache memory,register memory, and/or the like. As will be recognized, the volatilestorage or memory media may be used to store at least portions of thedatabases, database instances, database management systems, data,applications, programs, program modules, scripts, source code, objectcode, byte code, compiled code, interpreted code, machine code,executable instructions, and/or the like being executed by, for example,the processing element 205. Thus, the databases, database instances,database management systems, data, applications, programs, programmodules, scripts, source code, object code, byte code, compiled code,interpreted code, machine code, executable instructions, and/or the likemay be used to control certain aspects of the operation of the logisticsserver(s) 105 with the assistance of the processing element 205 andoperating system.

As indicated, in particular embodiments, the logistics server(s) 105 mayalso include one or more communications interfaces 220 for communicatingwith various computing entities, such as by communicatinginformation/data, content, information/data, and/or similar terms usedherein interchangeably that can be transmitted, received, operated on,processed, displayed, stored, and/or the like. Such communication may beexecuted using a wired information/data transmission protocol, such asfiber distributed information/data interface (FDDI), digital subscriberline (DSL), Ethernet, asynchronous transfer mode (ATM), frame relay,information/data over cable service interface specification (DOCSIS), orany other wired transmission protocol. Similarly, the logisticsserver(s) 105 may be configured to communicate via wireless externalcommunication networks using any of a variety of protocols, such asgeneral packet radio service (GPRS), Universal Mobile TelecommunicationsSystem (UMTS), Code Division Multiple Access 2000 (CDMA2000), CDMA20001× (1×RTT), Wideband Code Division Multiple Access (WCDMA), TimeDivision-Synchronous Code Division Multiple Access (TD-SCDMA), Long TermEvolution (LTE), Evolved Universal Terrestrial Radio Access Network(E-UTRAN), Evolution-Data Optimized (EVDO), High Speed Packet Access(HSPA), High-Speed Downlink Packet Access (HSDPA), IEEE 802.11 (Wi-Fi),Wi-Fi Direct, 802.16 (WiMAX), ultra wideband (UWB), infrared (IR)protocols, near field communication (NFC) protocols, Wibree, Bluetoothprotocols, wireless universal serial bus (USB) protocols, long range lowpower (LoRa), LTE Cat M1, NarrowBand IoT (NB IoT), and/or any otherwireless protocol.

Although not shown, the logistics server(s) 105 may include or be incommunication with one or more input elements, such as a keyboard input,a mouse input, a touch screen/display input, motion input, movementinput, audio input, pointing device input, joystick input, keypad input,and/or the like. The logistics server(s) 105 may also include or be incommunication with one or more output elements (not shown), such asaudio output, video output, screen/display output, motion output,movement output, and/or the like.

As will be appreciated, one or more of the logistics server(s)'s 105components may be located remotely from other logistics server(s) 105components, such as in a distributed system. Additionally oralternatively, the logistics server(s) 105 may be represented among aplurality of analysis computing entities. For example, the logisticsserver(s) 105 can be or be included in a cloud computing environment,which includes a network-based, distributed/data processing system thatprovides one or more cloud computing services. Further, a cloudcomputing environment can include many computers, hundreds or thousandsof them or more, disposed within one or more data centers and configuredto share resources over the network(s) 135. Furthermore, one or more ofthe components may be combined and additional components performingfunctions described herein may be included in the logistics server(s)105. Thus, the logistics server(s) 105 can be adapted to accommodate avariety of needs and circumstances. As will be recognized, thesearchitectures and descriptions are provided for exemplary purposes onlyand are not limiting to the various embodiments.

2. Exemplary Computing Entities

Computing entities 110 may be configured for registering one or moreusers, providing freight import cost information, processing one or moreshipping requests, securing parcels, monitoring shipments, and/or foroperation by a user (e.g., a vehicle operator, delivery personnel,customer, and/or the like). In certain embodiments, computing entities110 may be embodied as handheld computing entities, such as mobilephones, tablets, personal digital assistants, and/or the like, that maybe operated at least in part based on user input received from a uservia an input mechanism. Moreover, computing entities 110 may be embodiedas onboard vehicle computing entities, such as central vehicleelectronic control units (ECUs), onboard multimedia system, and/or thelike that may be operated at least in part based on user input. Suchonboard vehicle computing entities may be configured for autonomousand/or nearly autonomous operation however, as they may be embodied asonboard control systems for autonomous or semi-autonomous vehicles, suchas unmanned aerial vehicles (UAVs), robots, and/or the like. As aspecific example, computing entities 110 may be utilized as onboardcontrollers for UAVs configured for picking-up and/or deliveringpackages to various locations, and accordingly such computing entities110 may be configured to monitor various inputs (e.g., from varioussensors) and generated various outputs. It should be understood thatvarious embodiments of the present disclosure may comprise a pluralityof computing entities 110 embodied in one or more forms (e.g., parcelsecurity devices kiosks, mobile devices, watches, laptops, carrierpersonnel devices (e.g., Delivery Information Acquisition Devices(DIAD)), etc.)

As will be recognized, a user may be an individual, a family, a company,an organization, an entity, a department within an organization, arepresentative of an organization and/or person, and/or the like—whetheror not associated with a carrier. In particular embodiments, a user mayoperate a computing entity 110 that may include one or more componentsthat are functionally similar to those of the logistics server(s) 105.FIG. 3 provides an illustrative schematic representative of a computingentity 110 that can be used in conjunction with embodiments of thepresent disclosure. In general, the terms device, system, computingentity, entity, and/or similar words used herein interchangeably mayrefer to, for example, one or more computers, computing entities,desktops, mobile phones, tablets, phablets, notebooks, laptops,distributed systems, vehicle multimedia systems, autonomous vehicleonboard control systems, watches, glasses, key fobs, radio frequencyidentification (RFID) tags, ear pieces, scanners, imagingdevices/cameras (e.g., part of a multi-view image capture system),wristbands, kiosks, input terminals, servers or server networks, blades,gateways, switches, processing devices, processing entities, set-topboxes, relays, routers, network access points, base stations, the like,and/or any combination of devices or entities adapted to perform thefunctions, operations, and/or processes described herein. Computingentities 110 can be operated by various parties, including carrierpersonnel (sorters, loaders, delivery drivers, network administrators,and/or the like). As shown in FIG. 3 , the computing entity 110 caninclude an antenna 312, a transmitter 304 (e.g., radio), a receiver 306(e.g., radio), and a processing element 308 (e.g., CPLDs,microprocessors, multi-core processors, coprocessing entities, ASIPs,microcontrollers, and/or controllers) that provides signals to andreceives signals from the transmitter 304 and receiver 306,respectively. In some embodiments, the computing entity 110 includes oneor more sensors 330. In this way, the computing entity 110 is aspecial-purpose computer or particular machine that is configured tospecifically provide security for parcels. In some embodiments, at leastone of the computing entities 110 is coupled to the freight vessel 120(e.g., within the stem). The one or more sensors 330 can be one or moreof: a pressure sensor, an accelerometer, a gyroscope, a geolocationsensor (e.g., GPS sensor), a radar, a lidar, sonar, ultrasound, anobject recognition camera, and any other suitable sensor used to detectobjects or obtain information in a geographical environment that thefreight vessel is within. In some embodiments, the consolidation ofentries or purchase orders is based on this sensor information.

The signals provided to and received from the transmitter 304 and thereceiver 306, respectively, may include signaling information inaccordance with air interface standards of applicable wireless systems.In this regard, the computing entity 110 may be capable of operatingwith one or more air interface standards, communication protocols,modulation types, and access types. More particularly, the computingentity 110 may operate in accordance with any of a number of wirelesscommunication standards and protocols, such as those described abovewith regard to the logistics server(s) 105. In a particular embodiment,the computing entity 110 may operate in accordance with multiplewireless communication standards and protocols, such as UMTS, CDMA2000,1×RTT, WCDMA, TD-SCDMA, LTE, E-UTRAN, EVDO, HSPA, HSDPA, Wi-Fi, Wi-FiDirect, WiMAX, UWB, IR, NFC, Bluetooth, USB, and/or the like. Similarly,the computing entity 110 may operate in accordance with multiple wiredcommunication standards and protocols, such as those described abovewith regard to the logistics server(s) 105 via a network interface 320.

Via these communication standards and protocols, the computing entity110 can communicate with various other entities using concepts such asUnstructured Supplementary Service information/data (USSD), ShortMessage Service (SMS), Multimedia Messaging Service (MMS), Dual-ToneMulti-Frequency Signaling (DTMF), and/or Subscriber Identity ModuleDialer (SIM dialer). The computing entity 110 can also download changes,add-ons, and updates, for instance, to its firmware, software (e.g.,including executable instructions, applications, program modules), andoperating system.

According to particular embodiments, the computing entity 110 mayinclude location determining aspects, devices, modules, functionalities,and/or similar words used herein interchangeably. For example, thecomputing entity 110 may include outdoor positioning aspects, such as alocation module adapted to acquire, for example, latitude, longitude,altitude, geocode, course, direction, heading, speed, universal time(UTC), date, and/or various other information/data. In particularembodiments, the location module can acquire information/data, sometimesknown as ephemeris information/data, by identifying the number ofsatellites in view and the relative positions of those satellites (e.g.,using global positioning systems (GPS)). The satellites may be a varietyof different satellites, including Low Earth Orbit (LEO) satellitesystems, Department of Defense (DOD) satellite systems, the EuropeanUnion Galileo positioning systems, the Chinese Compass navigationsystems, Indian Regional Navigational satellite systems, and/or thelike. This information/data can be collected using a variety ofcoordinate systems, such as the Decimal Degrees (DD); Degrees, Minutes,Seconds (DMS); Universal Transverse Mercator (UTM); Universal PolarStereographic (UPS) coordinate systems; and/or the like. Alternatively,the location information can be determined by triangulating thecomputing entity's 110 position in connection with a variety of othersystems, including cellular towers, Wi-Fi access points, and/or thelike. Similarly, the computing entity 110 may include indoor positioningaspects, such as a location module adapted to acquire, for example,latitude, longitude, altitude, geocode, course, direction, heading,speed, time, date, and/or various other information/data. Some of theindoor systems may use various position or location technologiesincluding RFID tags, indoor beacons or transmitters, Wi-Fi accesspoints, cellular towers, nearby computing devices/entities (e.g.,smartphones, laptops) and/or the like. For instance, such technologiesmay include the iBeacons, Gimbal proximity beacons, Bluetooth Low Energy(BLE) transmitters, NFC transmitters, and/or the like. These indoorpositioning aspects can be used in a variety of settings to determinethe location of someone or something to within inches or centimeters.

The computing entity 110 may also comprise a user interface (that caninclude a display 316 coupled to a processing element 308) and/or a userinput interface (coupled to a processing element 308). For example, theuser interface may be a user application, browser, user interface,and/or similar words used herein interchangeably executing on and/oraccessible via the computing entity 110 to interact with and/or causedisplay of information from the logistics server(s) 105, as describedherein. The user input interface can comprise any of a number of devicesor interfaces allowing the computing entity 110 to receiveinformation/data, such as a keypad 318 (hard or soft), a touch display,voice/speech or motion interfaces, or other input device. In embodimentsincluding a keypad 318, the keypad 318 can include (or cause display of)the conventional numeric (0-9) and related keys (#, *), and other keysused for operating the computing entity 110 and may include a full setof alphabetic keys or set of keys that may be activated to provide afull set of alphanumeric keys. In addition to providing input, the userinput interface can be used, for example, to activate or deactivatecertain functions, such as screen savers and/or sleep modes.

As shown in FIG. 3 , the computing entity 110 may also include ancamera, imaging device, and/or similar words used herein interchangeably326 (e.g., still-image camera, video camera, IoT enabled camera, IoTmodule with a low resolution camera, a wireless enabled MCU, and/or thelike) configured to capture images. The computing entity 110 may beconfigured to capture images via the onboard camera 326, and to storethose imaging devices/cameras locally, such as in the volatile memory322 and/or non-volatile memory 324. As discussed herein, the computingentity 110 may be further configured to match the captured image datawith relevant location and/or time information captured via the locationdetermining aspects to provide contextual information/data, such as atime-stamp, date-stamp, location-stamp, and/or the like to the imagedata reflective of the time, date, and/or location at which the imagedata was captured via the camera 326. The contextual data may be storedas a portion of the image (such that a visual representation of theimage data includes the contextual data) and/or may be stored asmetadata (e.g., data that describes other data, such as describing apayload) associated with the image data that may be accessible tovarious computing entities 110.

The computing entity 110 may include other input mechanisms, such asscanners (e.g., barcode scanners), microphones, accelerometers, RFIDreaders, and/or the like configured to capture and store variousinformation types for the computing entity 110. For example, a scannermay be used to capture parcel/item/shipment information/data from anitem indicator disposed on a surface of a shipment or other item. Incertain embodiments, the computing entity 110 may be configured toassociate any captured input information/data, for example, via theonboard processing element 308. For example, scan data captured via ascanner may be associated with image data captured via the camera 326such that the scan data is provided as contextual data associated withthe image data.

The computing entity 110 can also include volatile storage or memory 322and/or non-volatile storage or memory 324, which can be embedded and/ormay be removable. For example, the non-volatile memory may be ROM, PROM,EPROM, EEPROM, flash memory, MMCs, SD memory cards, Memory Sticks,CBRAM, PRAM, FeRAM, NVRAM, MRAM, RRAM, SONOS, FJG RAM, Millipede memory,racetrack memory, and/or the like. The volatile memory may be RAM, DRAM,SRAM, FPM DRAM, EDO DRAM, SDRAM, DDR SDRAM, DDR2 SDRAM, DDR3 SDRAM,RDRAM, TTRAM, T-RAM, Z-RAM, RIMM, DIMM, SIMM, VRAM, cache memory,register memory, and/or the like. The volatile and non-volatile storageor memory can store databases, database instances, database managementsystems, information/data, applications, programs, program modules,scripts, source code, object code, byte code, compiled code, interpretedcode, machine code, executable instructions, and/or the like toimplement the functions of the computing entity 110. As indicated, thismay include a user application that is resident on the entity oraccessible through a browser or other user interface for communicatingwith the logistics server(s) 105 and/or various other computingentities.

In another embodiment, the computing entity 110 may include one or morecomponents or functionality that are the same or similar to those of thelogistics server(s) 105, as described in greater detail above. As willbe recognized, these architectures and descriptions are provided forexemplary purposes only and are not limiting to the various embodiments.

3. Exemplary Freight Vessel 120

FIG. 4 is a block diagram of the freight vessel 120 of FIG. 1 ,according to some embodiments. Although the freight vessel 120 isrepresented as a specific vehicle with specific sensors, it isunderstood that any suitable vehicle and/or sensor may exist. Forexample, the freight vessel 120 may represent or alternatively be anairplane or other vehicle configured to transport freight or goodsacross geographical boundaries (e.g., countries, states, etc.).

In various embodiments, the freight vessel 120 includes the lidar units404-1, 404-2, 404-3, the radar units 406-1 406-2, 406-3, the telematicsdevice 420, the camera(s) 402, and the computing device 430 (e.g., acomputing entity 110), such as an edge node. The lidar (Light Detectionand Ranging) units 404 are sensors that detect objects and build a mapof a geographical environment based on transmitting a plurality of lightpulses a second and measure how long it takes for those light pulses tobounce off of objects in the environment back to the sensor (e.g.,150,000 pulses per second). These lidar units, such as 404-1, canindefinitely spin transversely in a plane parallel to the groundcapturing a 360-degree image of the freight vessel 120's surroundings.The output is a three-dimensional mapping of the geographicalenvironment. These sensors can also calculate the distance betweenitself and the objects within the environment, as well as detectingexact sizes, colors, shapes of objects, and/or other metadata.

The radar units 406 are similar to the lidar units 404 in that they alsotransmit signals and measure how long these signals take to bounce offobjects back to the sensor. However, these signals are radio waves,instead of light pulses (which are faster). These sensors detect road orsea dynamics, such as detours, traffic delays, vehicle collisions, andother objects. Long range radar typically detects objects further awaycompared to lidar, which can be used for adaptive cruise control and thelike. Whereas lidar typically detects objects that are less far away andis used for emergency braking, pedestrian detection, collisionavoidance, etc.

The one or more cameras 402 utilize object recognition or computervision algorithms to detect and classify objects on the road, such aslane lines and traffic signs. These cameras can provide images to thecomputing device 430 for determining depth of field, peripheralmovement, and dimensionality of objects. In some embodiments, thesecameras 402 use deep learning or other machine learning models andtechniques for object classification. For example, in some embodiments,convolutional neural networks (CNN) are used to detect and classifyobjects, such as determining and classifying objects (e.g., car, person,traffic light, etc.). The one or more cameras 402 can be used forshort-distance recognition, such park assistance, compared to othersensors, such as lidar.

The telematics device 420 is configured to control a variety of vehiclesensors, collect vehicle telematics data generated by sensors, andtransmit the telematics data to the one or more analysis computingentities 105 and/or the computing entities 110 via one of severalcommunication methods. In various embodiments, the freight vessel 120 isequipped with one or more vehicle sensors (e.g., the vehicle's enginespeed sensor, speed sensor, seat belt status sensor, direction sensor,and location sensor). These sensors can detect one or more of thefollowing attributes: engine ignition (e.g., on or off), engine speed(e.g., RPM and idle time events), vessel speed (e.g., miles per hour),seat belt status (e.g., engaged or disengaged), vessel heading (e.g.,degrees from center), vessel backing (e.g., moving in reverse or notmoving in reverse), vessel doors (e.g., open or closed), vessel handles(e.g., grasped or not grasped by a driver), vessel location (e.g.,latitude and longitude), distance traveled (e.g., miles between twopoints), use of portable data acquisition device (e.g., in use or not inuse), throttle position, pedal position, and other measurements (e.g.,engine oil pressure, engine temperature, or engine faults). Thesesensors described above may be configured, for example, to operate inany fashion suitable to generate computer-readable data that may becaptured and transmitted by the telematics device 420.

In some embodiments, the telematics device 420 includes one or more ofthe following components, which are not shown: a processor, alocation-determining device or sensor (e.g., GPS sensor), a real-timeclock, J-Bus protocol architecture, an electronic control module (ECM),a port for receiving data from the vehicle sensors in one of thelogistics vehicles 120, a communication port for receiving instructiondata, a radio frequency identification (RFID) tag, a power source, adata radio for communication with a WWAN, a WLAN and/or a WPAN, FLASH,DRAM, and NVRAM memory modules, and a programmable logic controller(PLC). In an alternative embodiment, the RFID tag, the location sensor,and the PLC may be located in the logistics vehicle 120 external to thetelematics device 420. In various embodiments, the telematics device mayomit certain of the components described above. It should be understoodthat the telematics device may include any other suitable components.For example, the telematics device may include other types ofcommunications components than those described above.

According to one embodiment, a processor is configured to capture andstore telematics data from one or more vessel sensors (e.g., GPS sensor,lidar unit 404, radar unit 406-1, etc.) on a freight vessel 120 upon theoccurrence of one or more defined vehicle events. The processor isconfigured such that any parameter measurable by the one or more vehiclesensors may be defined as a vehicle event. The processor is alsoconfigured to associate telematics data received from the vehiclesensors 410 with contextual data indicating, for example: (1) the timethe data was captured (e.g., through time-stamping), (2) the vessel thedata was captured from, (3) the driver of the vessel, (4) a log reasonfor capturing the data, and/or (5) the route the driver was on at thetime the data was collected. In various embodiments, the processor isfurther configured to transmit the telematics data to the computingentity 110 and/or the one or more analysis computing entities 105. Inother embodiments, the processes described herein as being carried outby a single processor may be accomplished by multiple processors.

In one embodiment, the location sensor, which may be one of severalcomponents available in the telematics device 420, may be compatiblewith a low Earth orbit (LEO) satellite system or a Department of Defense(DOD) satellite system (e.g., via the satellite 112). Alternatively,triangulation may be used in connection with various cellular towerspositioned at various locations throughout a geographic area in order todetermine the location of the freight vessel 120 and/or its driver. Thelocation sensor 202 may be used to receive position, time, and speeddata. It will be appreciated by those skilled in the art that more thanone location sensor 202 may be utilized, and that other similartechniques may likewise be used to collect geo-location informationassociated with the freight vessel 120 and/or its driver.

In some embodiments, the ECM with J-Bus protocol may be one of severalcomponents available in the telematics device 420. The ECM, which may bea scalable and subservient device to the telematics device 420, may havedata processor capability to decode and store analog and digital inputsand ECM data streams from vehicle systems and the sensors. The ECM mayfurther have data processing capability to collect and present vehicledata to the J-Bus (which may allow transmittal to the telematics device420), and output standard vehicle diagnostic codes when received from avehicle's J-Bus-compatible on-board controllers or vehicle sensors.

In some embodiments, an instruction data receiving port may be one ofseveral components available in the telematics device 420. Embodimentsof the instruction data receiving port may include an Infrared DataAssociation (IrDA) communication port, a data radio, and/or a serialport. The instruction receiving data port may receive instructions forthe telematics device 420. These instructions may be specific to thefreight vessel 120 in which the telematics device 420 is installed,specific to the geographical area in which the freight vessel 120 willbe traveling, or specific to the function the freight vessel 120 serveswithin the fleet.

In some embodiments, a radio frequency identification (RFID) tag may beone of several components available for use with the telematics device420. One embodiment of the RFID tag may include an active RFID tag,which comprises at least one of the following: (1) an internal clock;(2) a memory; (3) a microprocessor; and (4) at least one input interfacefor connecting with sensors located in the freight vessel 120 or thetelematics device 420. Another embodiment of the RFID tag may be apassive RFID tag. One or more RFID tags may be internal to thetelematics device 420, wired to the telematics device 420, and/orproximate to the telematics device 420. Each RFID tag may communicatewirelessly with RFID interrogators within a certain geographical rangeof each other. RFID interrogators may be located external to the freightvessel 120 and/or within the computing entity 110 that can be carried inand out of the freight vessel 120 by the vehicle operator.

In some embodiments, vehicle performance and tracking data collected bythe telematics device 420 (e.g., telematics data) may be transmitted viaa WPAN to, and stored by, the computing entity 110 until a communicationlink can be established between the computing entity 110 and thelogistics server(s) 105, or similar network entity or mainframe computersystem. In one embodiment, the computing entity 110 may displaytelematics data for the driver's or other entity viewing, which may behelpful in troubleshooting vehicle performance problems and showingdelivery route progress, and/or consolidating entries. In an alternativeembodiment, the computing entity 110 may be a hand-held data acquisitiondevice, like an iPAQ. The Media Access Control (MAC) address, which is acode unique to each Bluetooth™-enabled device that identifies thedevice, similar to an Internet protocol address identifying a computerin communication with the Internet, can be communicated to other devicesin communication with the WPAN, which may assist in identifying andallowing communication among vehicles, cargo, and portable dataacquisition devices equipped with Bluetooth™ devices.

In some embodiments, the telematics device 420 receives the sensor datafrom the mapping sensors, such as the lidar units 404, radar sensors406, and camera 402 in order to consolidate entries. For example, insome embodiments, the telematics data described above is combined withthis mapping data from the sensors to provide additional insights, suchas average speed of the freight vessel 120. In some instances, forexample, it can be determined that customs agencies incorporate aspecific rule or have a certain risk of inspection on a particular day.Accordingly, these sensors may calculate the average speed of thefreight vessel 120, which can then be used to compute the day thefreight vessel 120 will arrive at the importing station, which may bethe day that the specific rule is followed or the risk of inspection isa particular risk value. Accordingly, this can be used to consolidateentries or purchase orders, as described in more detail below. Someembodiments additionally or alternatively send a control signal to thefreight vessel 120, which causes the freight vessel to activate orperform specific functionality associated with record consolidation. Forexample, using the illustration above, if the risk of inspection is highfor a particular day, then the one or more logistics server(s) 105 cansend a control signal to the freight vessel 120, which causes thefreight vessel 120 to speed up or slow down drive (e.g., via the lidarunits 404, radar sensor 406, and the camera 406) so as to miss the dayfor which the risk of inspection is high.

In various instances, buyers or sellers have to pay a MPF when importinggoods in the U.S. The MPF is a fee imposed by U.S. Customs and BorderProtection (CBP) for processing imports and allowing the release ofmerchandise into the United States. For formal entries (e.g.,merchandise value exceeding $2,500 USD), MPF is assessed on an advalorem (according to value) basis, at 0.3464% of the entered value,with a minimum of $25.67 SD and a maximum of $497.99 USD. MPF iscalculated by multiplying the shipment value by 0.3464%. For example,MPF on a shipment valued greater than $2,500 USD but less than $7,400USD will be assessed at the minimum fee of $25.67.

As described herein, existing computing tools make these staticcalculations to determine what the importing costs will be. Typically,these importing transactions come in the form of purchase orders, whichare logged in a database or data store as entries (e.g., either formalor informal). A purchase order (PO) (also referred to herein as a“record”) is a document that indicates an offer issued by a buyerindicating various attributes, such as type of product/service needed,quantity of product/service needed, and agreed-upon prices forproducts/services. These POs can be stored or mapped to entries.“Entries” in some embodiments are a declaration of information onimported or exported goods. Each entry typically includes one or morepurchase orders. An entry is typically the unit of information thatcustoms clearance agents analyze before the corresponding goods enter(and/or exit) a particular country (e.g., a port of entry). In someembodiments, an entry refers to or includes a bill of entry. A “bill ofentry” is a legal document filed by importers or customs clearanceagents on or before the arrival of imported goods described in the billof entry. Each bill of entry is submitted to the customs department aspart of the customs clearance procedure (i.e., before goods are allowedto enter into a country). Bill of entries are typically examined bycustoms clearance agents for its accuracy and conformity with tariffsand/or regulations. As described herein, some goods within entries arealso subject to inspection by these customs clearance agents. Entriescan include customs classification number, country of importing origin,description of the goods/services at issue (e.g., as indicated in theP.O.), quantity (e.g., as indicated in the P.O.) of goods, estimatedamount of duty to be paid, the particular POs within the entry, etc.

In some instances, importers may manually consolidate records so thatthey only have to pay a single maximum fee according to the MPF rulesdescribed above. For example, if a buyer knows that that two purchaseorders have a high value and is likely to be at or exceed $497.99, thebuyer may consolidate the POs so that the buyer only has to pay $497.99instead of double for the two separate POs at $995.98. However, theseMPF costs are not the only costs that importers are concerned with.Importers also have to pay for inspection fees. Customs inspections havean affinity for inspecting certain types of goods, while other goods arenot always inspected. In many cases the overall cost can beunpredictable given certain affinity for particular goods associatedwith the inspection fees and existing computing tools do notsufficiently predict the risk of inspection.

Besides the fees, another challenge with inspection fees is that ifparticular goods within an entry or PO are subject to an inspection,then the entire set of goods or services pertaining to the entry is heldor delayed for import or distribution because it is part of the entryfor the goods that were subject to an inspection. This delay can besubstantial (e.g., days or weeks), which can have a direct negativeimpact on the importer. The inspection fee challenge is compounded bythe fact that it may be desirable to consolidate entries, as describedabove. However, the more records that are consolidated, the higher riskthat at least one of the goods will be inspected and thus that theentire consolidated set of entries or associated goods will be delayedfor transportation and distribution. Accordingly, the consolidation ofrecords should be balanced with the risk of inspection while also takinginto account entity (e.g., customs) rules and other criteria, such asbuyer/seller requirements for consolidation of entries. A particulartechnical problem associated with these specific challenges are thatexisting computing tools do not adequately or intelligently predict therisk of inspection or how records should be consolidated based on thisrisk and other important factors, such as customs rules and specificbuyer/seller rules. As described above, these tools either rely on toomuch manual user input or do not adequately make predictions and arecomputing resource intensive.

Various embodiments of the present disclosure are directed to predictingwhich records should be consolidated based on one or more factors, suchas risk of inspection, customs rules, buyer/seller rules, learned data,and the Like, which is a particular solution to the technical problemdescribed above.

Turning now to FIG. 5 , FIG. 5 is a block diagram of an example systemarchitecture 500, in which embodiments of the present disclosure areemployed in, according to particular embodiments. In some embodiments,some or each of the components of FIG. 5 are included in the environment100 of FIG. 1 . For example, the components 504, 502, 506, 508, 510, and512 may all be modules stored on the one or more logistics servers 105.In another example, the buyer/seller data store 124 may correspond or bethe same as the buyer/seller data store 518. Likewise, the customs datastore 123 may corresponds or be the same as the customs data store 514.

The consolidation component 504 receives multiple inputs from one ormore of the other components and consolidates records to a singleinstance (e.g., record or entry) based on the multiple inputs.Alternatively or additionally, the consolidation component 504 refrainsfrom consolidating records based on the multiple inputs in someembodiments. In this way entries are consolidated (or not consolidated)based on multiple phases or signals, as illustrated in FIG. 5 . Forexample, the consolidation component 504 can receive the risk ofinspection from the risk of inspection component 502. The risk ofinspection component 502 calculates the risk of inspection estimate(e.g., a score) for a particular good or service and/or a particular POor entry (or consolidated set of POs for a given entry). For example,the component 502 may generate that a particular set of consolidated setof POs have a 90% of being inspected, being that one of the goods in theconsolidated entries, bananas, have a 90% chance of being inspected. Asillustrated in FIG. 5 , the risk of inspection can be based off ofexplicit customs rules 506, explicit buyer/seller rules, and orinformation learned by the learning module 510. In some embodiments, therisk of inspection component 502 uses one or more machine learningmodels via the learning module 510. For example, in some embodiments, aregression model is used to determine the probability of the risk ofinspection given information from the learning module 510, the explicitcustoms rule(s) 506, and/or the explicit buyer/seller rule(s) 506. Inother embodiments, a classification model or clustering model (e.g., asillustrated in FIG. 15 ) is used to determine risk of inspection classesor categories that are embedded in feature space based on their featurevalues and distance measures.

The explicit customs rules 506 are predefined rules that a customsagency (e.g., the CBP) enforces and thus are to be abided by. Forexample, a customs agency may require that a specific good is alwaysinspected regardless of where it came from. Another rule may be thatanother good is only inspected if it came from a specific set ofgeographic locations associated with pestilence or disease. In someembodiments, the explicit customs rules 506 are included in the customsdata store 514.

The explicit buyer/seller rules 508 are predefined rules that a sellerand/or buyer of imported goods enforces or desires to be enforced andthus are to be abided by. For example, a buying retailer entity mayprovide a rule that indicates that certain products or entries ofproducts are not to be consolidated (e.g., contained in the same entryfor importing). In another example, an entity may provide that certainproducts are always consolidated (e.g., always contained in the sameentry for importing). In some embodiments, the explicit byer/sellerrules 508 are included in the buyer/seller data store 514.

The learning module 510 parses or extracts features of historical data,learns (e.g., via machine learning model training) about the historicaldata by making observations or identifying patterns in data, and thenreceive a subsequent input (a current data point) in order to make adetermination, prediction, and/or classification of the subsequent inputbased on the learning. This learning information can then be passed tothe consolidation component 504 so that the consolidation component canconsolidate entries based on the data learned. In some embodiments, thelearning module 510 is or includes one or more machine learning models,such as a neural networks, random forest models, logistic regression,linear regression, k-nearest neighbor, Bayesian networks, and the like.For example, although not a rule defined in the rules 506 and 508, thelearning module 510 may identify a pattern in the customs data store 514that imports coming from country X have a 80% chance of an inspection,regardless of the particular good. In some embodiments, the learningmodule 510 uses a “genetic algorithm,” which generates groupconsolidation candidates, where each mutation or iteration has a fitnesslevel associated therewith. The more fit individuals (or factors (e.g.,inspection risk)) are, the more likely the particular factors will getpassed to the next generation. The fitness is typically the value of theobjective function in the optimization problem being solved. More fitindividuals are typically selected from a population. The new generationof candidate consolidation groups are then used in the next iteration ofthe algorithm.

As described above, in some embodiments the learning module 510 (and/orthe risk of inspection component 502) represents or uses one or moremachine learning models that train on data to make predictions. Trainingis the process of machine learning model learning or tuning such that aprediction statistic (e.g., a classification prediction, a regressionprediction, or clustering prediction) may become more increasinglyaccurate with higher confidence (and ideally with a lower error rate)after a particular threshold quantity of training sessions or epochs.For example, using the illustration above, the learning module 510 mayidentify a pattern, over various training stages, in the customs datastore 514 that imports coming from country X have an 80% chance of aninspection. The confidence level for this prediction may get higherafter each training stage and/or the actual chance of inspection maychange. For example, after a first training stage, the learning module510 may only predict that there is a 40% chance of inspection (asopposed to the last training stage, in which there is an 80% chance).

In some embodiments, training may include learning features (or featurevalues) of feature vectors (real numbers representing data) andresponsively weighting them during training. A “weight” in variousinstances corresponds to the importance or significant of a feature orfeature value for a decision statistic. For example, each feature may beassociated with an integer or other real number where the higher thereal number, the more significant the feature is for its classification.In some embodiments, a weight in a neural network or other machinelearning application can represent the strength of a connection betweennodes or neurons from one layer (an input) to the next layer (anoutput). A weight of 0 may mean that the input will not change theoutput, whereas a weight higher than 0 changes the output. The higherthe value of the input or the closer the value is to 1, the more theoutput will change or increase. Likewise, there can be negative weights.Negative weights proportionately reduce the value of the output. Forinstance, the more the value of the input increases, the more the valueof the output decreases. Negative weights may contribute to negativescores, which are described in more detail below. In many instances,only a selected set of features are primarily responsible for adetermination of a risk of inspection. In an illustrative example, afirst layer of nodes of a neural network may indicate the type of goodthat is to be inspected, where one node represents “fruit andvegetables” and another node represents “computer equipment” and anothernode represents “toys.” After various training stages, it may bedetermined that fruits and vegetables are inspected at a much higherrate than computer equipment and toys. Accordingly, the “fruit andvegetables” node can be activated or weighted much higher relative tothe other nodes, such that a prediction of an incoming data point (e.g.,a fruit record) can be predicted based on this weighting. Thus, overvarious training stages or epochs, certain feature characteristics foreach feature vector can be learned or weighted. In this way, embodimentslearn weights corresponding to different features such that similarfeatures found in different tables contribute positively to a predictionstatistic (e.g., a node is activated) and features that can changecontribute negatively to the prediction statistic (e.g., a node isinhibited).

This information predicted or identified by the learning module 510 maythen be passed to the risk of inspection component 502 and thenresponsively provided to the consolidation component 504. In anotherexample, the learning module 510 may predict, via analyzing thebuyer/seller data store 518, that a buyer will consolidate all of item Xin a purchase order based on past purchase order history of the sameconsolidation and/or based on the amount of sales of the good and thenon-risk of inspection associated with the particular good. In yetanother example, the learning module 510 may predict, via analyzing thecustoms data store 514, that a particular good will get inspected basedon historical dates of inspection of the particular, type of good, timeof year, who the inspector is, the country where the import originatedfrom, and/or the country where the import will arrive at.

The user feedback module 512 receives user input or selections andpasses those to the learning module so that the learning module 510 canfurther optimize learning. For example, at a first time the learningmodule 510 may predict that various entries should be consolidated basedon an explicit customer rule. However, a user with knowledge of a brandnew rule or other factor, can either input, via the user feedback module512, the new rule or the entries that are consolidated/not consolidatedbased on the new rule.

In some embodiments, the third party data store 516 corresponds to anydata that does not correspond do a custom entity or buyer/seller. Forexample, the third party data store can be any data that the learningmodule 510 uses to make predictions, such as unstructured social mediadata, blogs, e-books, news feeds. In some embodiments, alternatively oradditionally, the third part data store 516 corresponds to each of thesensor data obtained from the freight vessel 120 of FIG. 1 and/or FIG. 4. In this way predictions or consolidations of entries can be based atleast in part on outside or third party data, such as sensor data thatthe freight vessel 120 has captured.

The presentation component 520 provides an associated user interface,which is highly intuitive and user efficient, as described in moredetail herein. For example, the presentation component 520 can causedisplay of the risk of inspection associated with the risk of inspectioncomponent 502, consolidation entry candidates, rules, and the like. Thepresentation component 520 is generally responsible for structuring,tagging, or otherwise formatting information derived from theconsolidation component 504. For example, the presentation component 520can cause display of a user interface of a user device that includes theprediction made by the learning module 510 and the confidence level, theinformation determined by the risk of inspection component 502, and/orthe explicit customs rule(s) 506/explicit buyer/seller rule(s).

In some embodiments, the consolidation component 504 communicates, viaan application programming interface (API), to the presentationcomponent 520. In some embodiments, the presentation component 520includes one or more applications or services on a user device (e.g.,the mobile computing entity 110), across multiple user devices, or inthe cloud. For example, in some embodiments, presentation component 520manages the presentation of content to a user across multiple userdevices associated with that user. Based on content logic, devicefeatures, and/or other user data, presentation component 520 maydetermine on which user device(s) content is presented, as well as thecontext of the presentation, such as how (or in what format and how muchcontent, which can be dependent on the user device or context) it ispresented, when it is presented. In particular, in some embodiments,presentation component 520 applies content logic to device features, orsensed user data to determine aspects of content presentation.

In some embodiments, presentation component 520 generates user interfacefeatures. Such features can include interface elements (such as graphicsbuttons, sliders, menus, audio prompts, alerts, alarms, vibrations,pop-up windows, notification-bar or status-bar items, in-appnotifications, or other similar features for interfacing with a user),queries, and prompts. For example, the presentation component 220 canpresent the user interfaces as illustrated in FIG. 6 through FIG. 13 .

Alternative or in addition to the presentation component 520functionality, in some embodiments the consolidation component 504communicates, via an API, to a control signal propagator (not shown).This communication causes the control signal propagator to send acontrol signal to a machine, apparatus, or article of manufacture, whicheffectively and tangibly causes such machine, apparatus, or article ofmanufacture to activate or otherwise perform a particular function basedat least in part on the functionality performed by the consolidationcomponent 504. For example, the control signal propagator can send acontrol signal to a computing device (e.g., the mobile computing entity110) causing an auditory (e.g., a beeping sound), visual (e.g., flashingLEDs), buzzing/vibrating, and/or other alert type based on theconsolidation component 504 consolidating or not consolidating records(or recommending to do such action). For instance, the consolidationcomponent 504 may determine that the risk of inspection is high (via therisk of inspection component 502) and responsively send acomputer-readable set of instructions to a device, causing a device togenerate an alert (e.g., an audio or visual alert) of the high risk ofinspection, as well as a prompt to receive user approval to consolidatethe entries. In some embodiments, in response to receiving such approvaluser input, the consolidation component 504 may consolidate entries. Inanother example, if the learning module 510 predicts, via analyzingpatterns in the customs data store 514, that there will be a high riskof inspection if goods arrive at the inspection center at a particularday, the consolidation component 505 (or control signal propagator) cansend a control signal to the freight vessel 120 to change its course(e.g., speed up or slow down via its driverless sensors) so that it doesnot arrive at the particular day.

IV. EXEMPLARY SYSTEM OPERATION

FIG. 6 is a screenshot 600 of a user interface indicating particularvessels that have been optimized for consolidation, according to someembodiments. In some embodiments, the screenshot 600 (or any screenshotdescribed herein) is provided by a logistics entity, such as by thelogistics server(s) 105 (e.g., over the network(s) 135 to the computingentity 110). In particular embodiments, the screenshot 600 (or anyscreenshot described herein) is provided to any suitable entity, such asone or more of the computing entities 110, the presentation component520, and/or the freight vessel 120. The screenshot 600 (or anyscreenshot described herein) can be accessed or provided in any suitablemanner. For example, in some embodiments, a user can open a clientapplication, such as a web browser, and input a particular UniformResource Locator (URL) corresponding to a particular website or portal.In response to receiving the user's URL request, an entity, such as theone or more analysis computing entities 105 may provide or cause to bedisplayed to a user device (e.g., a computing entity 110), thescreenshot 700. A “portal” as described herein in some embodimentsincludes a feature to prompt authentication and/or authorizationinformation (e.g., a username and/or passphrase) such that onlyparticular users (e.g., a corporate group entity) are allowed access toinformation. A portal can also include user member settings and/orpermissions and interactive functionality with other user members of theportal, such as instant chat. In some embodiments a portal is notnecessary to provide the user interface, but rather any of the views canbe provided via a public website such that no login is required (e.g.,authentication and/or authorization information) and anyone can view theinformation. In yet other embodiments, the screenshot 600 represents anaspect of a locally stored application, such that a computing devicehosts the entire application and consequently the computing device doesnot have to communicate with other devices (e.g., the logisticsserver(s) 105) to retrieve data. In various embodiments, thepresentation component 520 determines or provides each of the datadescribed in the screenshot 600 (or any screenshot described herein)

The screenshot 600 includes various attributes or columns, such as thevessel name, SCAC, Estimated Arrival Date, Discharge Port, DestinationPort, PO Count, Status, and Actions. Each record or row within thescreenshot corresponds to a particular freight vessel (e.g., freightvessel 120). The “vessel name” attributes corresponds to the ID of theparticular freight vessel. The “SCAC” identifier represents a “StandardCarrier Alpha Code,” which is an identifier used to identify roadtransport companies. In some instances, vessel operating common carriers(VOOCs) have SCAC codes. The “Estimated Arrival Date” attributecorresponds to an estimated arrival of the particular vessel carryingcargo or imports. In some embodiments, the “Estimated Arrival Date” ispredicted by the learning module 510, as described with respect to FIG.5 . In some embodiments, the “discharge port” attribute identifies theparticular port where a particular vessel will unload its cargo, whichis then dispatched to consignees. In some embodiments, the “destinationport” attribute identifies the final arrival point of a shipment. The“PO count” attribute corresponds to the purchase order count or thequantity of POs that have been consolidated (e.g., as determined by theconsolidation component 504). The “Status” attribute indicates whetherentries or POs for the particular vessel record have been “optimized.”In some embodiments, “optimized” refers to an indication of whether thesystem has intelligently generated consolidation groups based onmulti-phase factors, such as generating different candidate entries ofconsolidation to determine the most optimal grouping of POs or entries.For example, optimization may indicate whether the consolidationcomponent 504 has consolidated entries based on the inputs received fromthe rest of the components. The “Actions” attribute corresponds toadditional information associated with the vessel record that may beaccessed via drill down, for example, as described in more detail below.

FIG. 7 is a screenshot 700 of an example user interface illustratingoptimization details, according to some embodiments. In theseembodiments, optimization has already occurred (e.g., the consolidationcomponent 504 has generated candidate entry groupings), and the userinterface of FIG. 7 is responsively generated. In some embodiments, thescreenshot 700 is rendered or provided in response to receiving a userselection of the UI element 603 under the “Action” attribute for one ofthe records illustrated in FIG. 6 where the record has been “optimized.”For example, in response to receiving a selection of the UI element 603for the record vessel name “PARSIFAL,” the screenshot 700 may be causedto be displayed, indicating more detail associated with the “PARSIFAL”record. In some embodiments, the “Recommended MPF” and “Recommendedsavings” values correspond to what the MPF is projected to be afteroptimizations (e.g., functionality performed by the consolidationcomponent 504), and the predicted savings. In some embodiments, the“Custom MPF” and “Custom Savings” values correspond to user changes tothe optimization logic or “Recommended MPF” or “Recommended Savings”figures, as described in more detail herein.

The screenshot 700 also illustrates various entry records mapped to POs.Accordingly, a particular entry may include one or more purchase orders.For example, entry 40 is mapped to one or more purchase orders. Invarious embodiments, in response to receiving a user selection of the“View” button (e.g., the button 705) under the POs attribute, the systemcauses each of the purchase orders that were consolidated to thecorresponding entry (e.g., entry 40) to be displayed. In this way, userscan see what purchase orders have been consolidated based on theoptimization logic. In some embodiments, in response to receiving a userselection of the “customize” button 703 in the screenshot 700, a userinterface is provided to allow the user to manually re-arrangeconsolidation. For example, the consolidation component 504 may haveconsolidated 3 purchase orders to entry 40. However, the user may decideshe does not want one of the purchase orders consolidated. Accordingly,particular embodiments receive a user selection or other user input toremove one of the three purchase orders from consolidation.Responsively, the user feedback module 512 may receive and store, inmemory, the user input such that there is a new consolidation outputcaused to be displayed in response to receiving a selection of thebutton 705.

FIG. 8 is a screenshot 800 of an example user interface illustratingpurchase orders that are consolidated to particular entries and how theuser can change or override optimization results via the user interface,according to some embodiments. In some embodiments, these changes areuploaded by the user feedback module 512. In some embodiments, thescreenshot 800 (and/or the data under the “custom” attribute (e.g.,including record 805)) is caused to be provided in response to receivinga selection of the “customize” button 703 of the screenshot 700 of FIG.7 . The “POID” corresponds to the purchase order ID. FIG. 8 illustrates,among other things, that the optimization system (e.g., theconsolidation component 504) recommended consolidating purchase orderIDs 123, 124, and 125 to entry 40, as illustrated in the “Recommended”box 803. In some embodiments, the box 803 is caused to be displayed inresponse to receiving a selection of the view button 705 of FIG. 9 .Likewise, in some embodiments, in response to receiving a user selectionof the “hide” button 809, particular embodiments cause the box 803 toidentically be displayed relative to FIG. 7 .

As illustrated in the “Custom” box 807, POID 6756 has additionally beenadded to entry 40. As described above, “custom” functionality in variousembodiments, allows a user to make changes to optimization, such asadding purchase orders to particular entries, as illustrated in thecustom box 807 of FIG. 8 . Alternatively or additionally, users can makeother changes, such as removing purchase orders, changing rules used foroptimization, etc. In this way, users can drag or drop POIDs to createnew consolidation arrangements. In some embodiments, in response toreceiving such drags or drops or other user changes, the system updatesand provides for display the costs and/or savings associated therewith(e.g., the “Custom MPF” and “Custom Savings” values as illustrated inFIG. 7 ). In some embodiments, alerts are also generated or provided fordisplay in response to a detection of rules or constraints (e.g., fromthe explicit customs rules 506 or the explicit buyer/seller rules 508)being broken (and/or followed). In some embodiments, these alerts arealso generated and displayed anytime there is a high-risk item (e.g.,item having a high risk of inspection) being consolidated manually bythe user. For example, the notification 811 stating “WARNING: Added itemPGA flag is true” may indicate that the newly added POID 6756 has a highprobability of inspection or that at Partner Government Agency regulatesa specific good to be imported, thereby leading to a determination ofhigh probability of inspection.

In some embodiments, the system (e.g., the consolidation component 504)uses user changes or selections to further optimize or learn what theparticular user does. For example, a machine learning algorithms mayidentify patterns or associations that the user makes over time withrespect to the “Custom” box, so that the system can continually optimizefunctionality. In this way, for example, the learning module 510 canreceive user selections via the user feedback module 512 to continuallylearn so that the consolidation component 504 can generate current orfuture consolidated groups of records (or refrain from consolidatingrecords) based on the user feedback from prior sessions or history ofuser selections. In response to receiving a user selection of the UIelement 813, particular embodiments reset record consolidation back to adefault setting. For example, in response to receiving a user selectionof the UI element 803, particular embodiments remove record POID: 6756from the entry 40 such that it reflects the recommended box 803.Alternatively, the recommended box 803 can remove all of its POIDs(i.e., POIDs 123, 124, and 126) such that there is not consolidation.

FIG. 9 is a screenshot 900 of an example user interface that illustratesa historical view that tracks cumulative savings over a period of time,after optimization has occurred (e.g., after the consolidation component504 has generated recommended PO groupings and the user has madeselections via the “Customs” UI portion of FIG. 8 ), according to someembodiments. The “Vessel” name field 903 allows the user to enter in theparticular vessel ID to be searched so that the cumulative savings canbe indicated over a period of time. The “date range” UI element 905allows the user to select the time range for the cumulative savings fora particular vessel. The “methods” UI element 907 and the “Company Xrules” UI elements 909 are configured to receive user selections orimplement particular buyer/seller rules so that the corresponding datacan be viewed in the chart 911 and under the optimization history 913.For example, in response to receiving a selection of the “Enforced”identifier under the “Methods” UI element 907 (and not any otheridentifiers), the screenshot 900 may provide cumulative savings for onlythe “enforced” rules (and not the “relaxed” or “fixed” rues). FIG. 9illustrates that all three “Enforced,” “Relaxed,” and “Fixed” identifierfields have been selected such that all the costs and savings associatedtherewith and corresponding graphs are displayed to the screenshot 900.

Under the UI element 909, there are various listed Customer X rules(e.g., corresponding to the buyer/seller data store 125 and/or theexplicit buyer/seller rules 508). The first rule reads “60 PO max perentry” which indicates that no more than 60 purchase orders may beconsolidated to a single entry. The second rule reads “No PGA itemsconsolidate” which indicates that no purchase orders and/or goods thatare subject to regulation by PGA are not to be consolidated (or only PGAitems can be consolidated together). The Third rule reads “excludespecified event codes (e.g., class 60 car accessories and car partsdescribed by National Engine Freight Classification (NMC)) fromconsolidation” which may be indicative of excluding certain classes ortypes of goods from consolidation. The fourth rule reads “Exclude POsbelow $2,500), which specifies to not consolidate (or refrain fromconsolidating) any purchase orders below this amount. The fifth rulereads “Exclude anti-dumping and Countervailing,” which is indicative ofexcluding goods for consolidation that are or maybe subject toanti-dumping and/or countervailing duties or fees. Anti-dumping (AD) andCountervailing (CV) corresponds to additional fees that the U.S.Department of Commerce uses to discourage demand for goods deemed to beimport sensitive. AD duties are assessed when it is determined thatforeign suppliers or manufacturers are selling goods in the U.S. atless-than-fair market value. CV duties are applicable when a foreigngovernment provides subsidies or assistance to a local industry (e.g.,low-rate loans, tax exemptions, etc.). This assistance enables thesesuppliers and manufacturers to potentially export and sell goods forless than domestic (U.S.) companies. The sixth rule reads “ExcludeInformal Lines,” which is indicative of excluding particular freightlines or shipping companies (e.g., the identity of which may bedetermined from the sensor data from the sensors described in FIG. 4 ).The sixth rule reads “Exclude ‘feather’ product descriptions,” which isindicative of excluding any good from consolidation where thedescription includes the term “feather.” As indicated in the UI element909, one or more fields corresponding to any of these rules may beselected, each of which, when selected, change the output graph andsummary 911.

In some embodiments, the “enforced” identifier indicates particularbuyer/seller rules that are enforced or followed. In some embodiments,the “Relaxed” identifier indicates particular buyer/seller rules thatare soft rules or not always enforced. In some embodiments, the “Fixed”identifier indicates that all rules other than buyer/seller (e.g.,customs agency rules) are enforced or followed. In some embodiments, the“Fixed” identifier alternatively or additionally indicates that a ruleis not ever modifiable (e.g., a rule that cannot be relaxed). For eachidentifier, particular costs, savings, are caused to be displayed. Forexample, the screenshot 900 indicates that when the rules were“Relaxed,” there was the most savings, compared to when the rules werestrictly “Enforced” or “Fixed.”

The cumulative savings UI element 911 includes a graph and statics thatillustrate the savings in money made for consolidating POs underenforced, relaxed, and/or fixed methods according to the selection madeunder the element 907. The “method” attribute indicates the type ofmethod selected under the UI element 907. The “theoretical max”attribute indicates the total amount of money it will cost to importgoods associated with one or more consolidated purchase orders for agiven date range (e.g., April 1 through June 30^(th) as illustrated inthe graph) for a given method or rule constraints. The “company X cost”indicates the amount of money it will cost a particular buyer/seller ofcorresponding purchase orders for the given date range and method orrule constraints. The “potential savings” attribute indicates thepotential savings in dollar amount by consolidating particular POs toparticular entries for a given date range and rule constraints. The“percentage savings” attribute indicates the potential savings inpercentage by consolidating particular POs to particular entries for agiven date range and rule constraints. The optimization history 913generally indicates a summary of the shipments or vessels of shipmentscoming in, along with the expected costs and savings for the respectiveshipment or vessels.

FIG. 10 is a screenshot 1000 of an example user interface thatillustrates costs associated with consolidating and not consolidatingpurchase orders, according to some embodiments. In some embodiments, thesystem (e.g., the presentation component 520) causes display of thescreenshot 1000 in response to receiving a selection of the “incoming”button 915 of FIG. 9 . In some embodiments, the screenshot 1000represents the optimization history 913 of FIG. 9 . The screenshot 1000summarizes the shipments or vessels of shipments coming in, along withthe expected costs and savings for the respective shipment or vessels.In some embodiments, optimization (e.g., the consolidation component 504functionality) has already occurred and FIG. 10 is responsivelygenerated. In some embodiments, a selection of any of the widgets,records, or attributes in FIG. 10 causes a specific vessel or shipmentview to be displayed.

The “Vessel” attribute within the screenshot 1000 indicates the vesselID for which one or more POs were consolidated. As described above, he“SCAC” attribute indicates a “Standard Carrier Alpha Code,” which is anidentifier used to identify road transport companies used for the givenvessel on the given date. The “date” attribute indicates the date atwhich a particular optimization (e.g., a consolidation performed by theconsolidation component 504) for a particular vessel on a particulardate occurred on. Alternatively or additionally the date can correspondto the date at which corresponding goods were loaded for importing. The“co. X MPF” attribute indicates what the MPF fee due for importing theassociated freight to/from company X would be if there were nooptimizations (e.g., consolidations performed by the consolidationcomponent 504). The “co. X expected cost attribute” indicates what thetotal cost for importing freight would be to/from company X if therewere not optimizations for the particular date and Vessel. The “co. Xrealized MPF savings” attribute indicates the MPF savings, in dollars,would be if there were not optimizations performed. The “optimized MPF”attribute indicates what the MPF cost would be for company X on thegiven date for the given vessel assuming one or more optimizations occur(e.g., consolidations generated by the consolidation component 504). The“optimized expected cost” indicates what the total cost would be forcompany X on the given date for the given vessel assuming one or moreoptimizations occur. The “optimized cost savings” attribute indicateswhat the savings, in dollars, would be if a set of records areconsolidated to one or more entries for the particular date on the givenvessel for company X. The “method” attribute corresponds to therule-type constraints, as described with respect to the UI element 907.The “percent cost savings” attribute indicates what the savings, inpercentage, would be if the set of records are consolidated to one ormore entries for the particular date on the given vessel for company X.In this way, user experiences can be improved because the user can beshown a summary page or window (i.e., the screenshot 1000) thatsummarizes each purchase order or entry generated and theircorresponding cost information both with and without optimization.

FIG. 11 is a screenshot 1100 of an example use interface that maps costsand savings associated to various entries, according to particularembodiments. In some embodiments, the screenshot 1100 is caused to bedisplayed in response to a receiving a user selection of a particularrecord associated with a vessel of FIG. 10 . FIG. 11 lists each entry orconsolidated entry for a given vessel, indicated by the “ENTRY ID”attribute. The “PO COUNT” attribute corresponds to the quantity ofpurchase orders consolidated to a particular entry. The “Inspectionprobability” attribute in various embodiments corresponds with theprobability that the entry or set of POs will get inspected (e.g., asdetermined by the risk of inspection component 502). The “PO TOTAL”attribute in some embodiments corresponds to the total value of thegoods within all of the consolidated POs for a particular entry. Forexample, each PO can have an agreed upon purchase amount. If each agreedupon purchase amount can be added up, this can represent the PO total.The “TOTAL MPF AMOUNT” attribute in some embodiments refers to the MPFprice amount for a given entry. The “MPF SAVINGS” in some embodimentscorresponds to the amount of MPF saved based on the system optimizing(e.g., the system 500 performing its functionality). In variousembodiments, in response to receiving a selection of an “Actions”identifier (e.g., identifier 1103) within a record, the system causes aview to be displayed of each purchase order within an entrycorresponding with the record selected.

The header 1105 of the screenshot 1100 indicates a vessel identifierand/or a SCAC identifier (e.g., as indicated in the “vessel” and “SCAC”attributes in FIG. 10 ), as well as the type of rule constraint(“fixed”), and the date that the vessel will arrive (or has left) forinspection. The body 1107 indicates the “total entries,” which is thetotal quantity of entries for corresponding purchase orders and goodsthat the vessel will carry for the particular date. The body 1107 alsoindicates the “optimized cost savings,” which is the total amount, indollars, that is saved by optimizations performed (e.g., by theconsolidation component 504) for some or all of the entries for thegiven vessel on the given date. The “MPF and inspections cost” indicatesthe total amount it will cost a customer (which includes the MPF cost orfee plus inspection costs) for all goods of entries the customer haspurchased that are on the particular vessel for the particular date,which takes into account optimizations or consolidation savings.

FIG. 12 is a screenshot 1200 of a user interface that illustratesspecific purchase order information for a given entry, according to someembodiments. In some embodiments, the screenshot 1200 is caused to bedisplayed in response to receiving a selection of the “Actions”identifier 1103 for ENTRY ID 34 of FIG. 11 . FIG. 12 illustratesdetailed information for each purchase order that has been consolidated(e.g., via the consolidation component 504) to entry 34. In someembodiments, the “NUM ITEMS” attribute corresponds to the quantity oftypes of goods or items for a given entry of PO. For example, the entrymay include fruits, toy cars, and gloves corresponding to a NUM ITEMScount of 3. The “PURCHASE ORDER NUMBER” corresponds to the purchaseorder ID. The “BILL OF LADING” corresponds to an identifier for aparticular bill of lading of a given entry or PO. A bill of lading is adocument issued by a carrier to acknowledge receipt of cargo forshipment. The bill of lading can include shipper and carrier details,such as goods that are shipped, where shipment is going or coming from,and the like. The bill of lading can be proof that the carrier hasreceived the freight in suitable condition, as provided by the shipper.The bill of lading can also indicated that the goods may be transferredto the holder of the bill of lading (e.g., the carrier) to betransferred to someone else, such as the consignee. The “PO value”indicates the cost of the purchase order, which may be a sub-cost of the“PO TOTAL” as described above with respect to FIG. 11 . The “PGA ITEMFLAG” attribute in some embodiments refers to a quantity of rules orconstraints that are to be enforced for the entry ID 34 or purchaseorder (e.g., the rules indicated within the explicit customs rules 505and/or the explicit buyer/seller rules 508). In some embodiments, the“EVENT” attribute corresponds to different sets of the same purchaseorder. In some embodiments, in response to receiving a user selection ofthe “Show Invoice” attribute, a specific invoice is displayed for thegiven PO.

FIG. 13 is a screenshot 1300 of an example user interface illustratinginvoice details 1303 of a particular purchase order, according to someembodiments. In some embodiments, the screenshot 1300 is provided inresponse to receiving a selection of the “View” button 1203 for one ofthe records or POs within the screenshot 1200 of FIG. 12 . In someembodiments, the “total value” attribute is indicative of the total costto import or buy (or the total value of) particular items (e.g., goods)or item types, where each record corresponds to an item/item type. Forexample, for purchase order number 5217962181, there are items that eachhave a particular “total value.” In some embodiments, the “tariffnumber” attribute corresponds to the tariff code or product-specificcode as documented in the Harmonized System (HS) maintained by the WorldCustoms Organization (WCO). Tariff codes may exist for virtually everyproduct/good involved in global commerce. These codes are typicallyrequired on official shipping documents for tax assessment purposes. Insome embodiments, the “description” attribute corresponds to a naturallanguage description of the corresponding item or product/good. Theinvoice details 1303 further illustrates individual invoice line items,including inspection probability. In some embodiments, the “inspect probline” attribute corresponds to inspection probabilities of any givenitem or product/good used to calculate inspection probabilities (e.g.,by the risk of inspection component 502) for associated purchaseorders/entries.

FIG. 14 is a flow diagram of an example process 1400 for consolidatingitems, according to some embodiments. The process 1400 (and/or any ofthe functionality described herein) may be performed by processing logicthat comprises hardware (e.g., circuitry, dedicated logic, programmablelogic, microcode, etc.), software (e.g., instructions run on a processorto perform hardware simulation), firmware, or a combination thereof.Although particular blocks described in this disclosure are referencedin a particular order at a particular quantity, it is understood thatany block may occur substantially parallel with or before or after anyother block. Further, more (or fewer) blocks may exist than illustrated.For example, in some embodiments, block 1404 and/or 1402 is notperformed. In another example, there are other added blocks, such assending a control signal to activate a device in response to block 1408.Such added blocks may include blocks that embody any functionalitydescribed herein. The computer-implemented method, the system (thatincludes at least one computing device having at least one processor andat least one computer readable storage medium), and/or the computerprogram product/apparatus as described herein may perform or be causedto perform the process 1400, and/or any other functionality describedherein.

Per block 1402, one or more rule data sets are received (e.g., explicitrules 506 and/or 508). For example, the rules can be buyer, seller, andor customs rules for consolidating records or POs (purchase orders). Insome embodiments, the rule data sets include one or more rules from acustoms agency, as described for example with respect to the customsdata store 123 and/or the explicit customs rule(s) 506 of FIG. 5 . In anillustrative example, a rule can be received that specifies certaintypes of goods (e.g., fruit) are inspected and/or items from certaincountries are always inspected. In some embodiments, the rule data setsinclude one or more rules from a buyer (and/or seller) associated withthe plurality of records, as described for example, with respect to thebuyer/seller data store 125 and/or the explicit buyer/seller rule(s)508. In some embodiments, the rule data sets are associated withconstraints controlling how records are to be consolidated, where therecords include attributes of goods (e.g., POs) that are to be receivedfor importing. For example, the one or more rules may include one ormore rules similar or identical to the rules specified under the UIelement 909 (e.g., “60 POs max per entry.”).

Per block 1404, one or more extracted features are processed or runthrough a machine learning model. In some embodiments, this includesextracting features from historical data and processing the extractedfeatures through a machine learning model. Examples of this aredescribed with respect to the risk of inspection component 502, thelearning module 510, and the feature space visualization 1500 of FIG. 15where embodiments derive a feature vector in order to generate aprediction. In some embodiments, this “historical data” includes pastpurchase orders that have been labeled as “inspected” or “notinspected,” as described with respect to FIG. 15 , for example.

In block 1404, learned information may be obtained (e.g., from thelearning module 510). For example, a machine learning model can learnparticular patterns or associations of customs agencies that are notnecessarily hard rules, such as identifying that customs agencies tendto inspect a particular good above a threshold only a particular time ofyear, such as a season or other time window (e.g., days, weeks, months,etc.). These predictions or calculations can occur via any suitableprobabilistic model, such as a Bayesian network, a TAN model, NaïveBayes classifier, a factor graph, a clique tree, Markov random field, achain graph, or any other suitable technique. For example, in someembodiments, predictions include using a Bayesian network graph. ABayesian network graph is a directed acyclic graph that maps therelationships between nodes (e.g., events) in terms of probability.These graphs show how the occurrence of particular events influence theprobability of other events occurring. Each node is also conditionallyindependent of its non-descendants. These graphs follow the underlyingprinciple of Bayes' theorem, represented as:

$\begin{matrix}{{{P\left( {A❘B} \right)} = \frac{{P\left( {B❘A} \right)}{P(A)}}{P(B)}},} & {{Equation}1}\end{matrix}$

where A and B are events and P(B)≠0. That is, the probability (P) of Agiven B=the probability of B given A multiplied by the probability of(A) all over the probability of B.

Per block 1406, the risk of inspection of goods are determined (e.g., asdescribe with respect to the risk of inspection component 502). In someembodiments, block 1406 includes generating an estimate score (e.g., aninteger) that is indicative of the risk for inspection for a first setof goods that are to be imported. For example, referring back to theexample of block 1402, a rule can be received that specifies certaintypes of goods (e.g., fruit) are inspected. And that certain type ofgood is to be imported. Accordingly, embodiments determine that there isa 100% chance of inspected (or near 100% chance) and responsively scorethe good (or associated PO since it contains the good) a 10 on a scaleof 1 to 10, which 10 being the highest likelihood of inspection.

In some embodiments, the estimate score is generated based at least inpart on the receiving of the rule data sets and/or the processing of thefeatures through the machine learning model (blocks 1402 and 1404). Forexample, using the illustration above in block 1404, a machine learningmodel can be used to derive and embed a feature vector indicating thatcustoms agencies tend to inspect a particular good above a thresholdonly a particular time of year. However, it may not quite be theparticular time of year but the particular good may be the same as thegood analyzed at block 1406. Accordingly, the classification may be“intermediate risk of inspection” (e.g., as described with respect toFIG. 15 ). Using the illustration above per block 1402, it may be alsodetermined that a customs rule may specify that certain goods fromcertain countries are always inspected. Even though the goods at block1406 may match these certain goods, the country of origin may not matchsuch that they are not always inspection. Accordingly, the risk ofinspection component can downgrade, weight, or otherwise change theoriginal “intermediate risk of inspection” prediction even lower, sincethe goods and country to not violate the customs rule.

In another example, it can be determined that because one of the ruledata sets (per block 1402) indicate that buyer X always wants good Y tobe consolidated, good Y is on a current shipment to be imported, andgood Y has a high probability of inspection. Additionally oralternatively, it can be determined (e.g., via block 1404) that good Yis always inspected on during December, and it may happen to be Decemberand the same good Y may be up for inspection. Accordingly, the risk ofinspection is deemed to be high. In some embodiments, even though blocks1402 and 1404 are performed, the actual risk of inspection per block1406 may be based only on a single block, but not both.

Per block 1408, one or more records (e.g., purchase orders) areconsolidated (or recommended to be consolidated) to a single instance(e.g., an entry or purchase order) (e.g., as described with respect tothe consolidation component 504). In some embodiments, based at least inpart on the generated estimate score, embodiments can consolidate (orrefrain from consolidating) the plurality of records to the singleinstance for the first set of goods. For example, each of the goodsdescribed with respect to block 1406 may be identified in one or morepurchase orders. Particular embodiments recommend refraining fromconsolidating purchase orders if any good identified in any of thepurchase orders has a risk of inspection score over a thresholdindicating that the risk of inspection of the good is high. Someembodiments recommend consolidating purchase orders if all of the goodsidentified in each of the purchase orders have a risk of inspectionscore below a threshold indicating that the risk of inspection of all ofthe goods in all purchase orders is low.

Consolidation (or recommendation of consolidation) of records occursbased on the rule data sets, the obtained learned information, and/orthe risk of inspection corresponding to the blocks 1402, 1404, and 1406respectively. In some embodiments, the consolidation of recordsadditionally or alternatively occurs based at least in part on receivinguser selections or information from the user feedback module 512. Insome embodiments, the consolidation of records is additionally oralternatively occurs based on the third party data store 516 or thesensor data obtained from the freight vessel 120. Each of these phasesor signals in various embodiments is weighted or scored based onimportance or ranking. For example, customs rules may be weighted higherthan buyer/seller rules or any sensor data obtain from a freight vessel.In some embodiments, the consolidation of records per block 1408 isproceeded by scoring different candidate single instance groups andselecting the instance(s) with a score above a threshold.

In some embodiments, the plurality of records is indicative of aplurality of purchase orders and the single instance is indicative of asingle entry, as described, for example, with respect to FIG. 6 throughFIG. 13 . In some embodiments, the consolidating at block 1408 (and/orthe determining of the risk of inspection per block 1406) is furtherbased at least in part on sensor data obtained from the freight vessel(e.g., as described with respect to FIG. 4 ). For example, one of therules or learned data indicates that the risk of inspection is high on aparticular Monday. Embodiments can receive telematics device informationor other sensor data described with respect to FIG. 4 to determine thespeed of the vessel to determine the predicted arrive at a port of entryfor inspection. Based on this information, it can be determined that thevessel will arrive on the particular Monday given the sensor data,thereby predicting that the inspection risk is high and therebyselectively refraining from consolidating entries.

In some embodiments, the consolidation at block 1408 occurs iterativelysuch that there are groups of candidate consolidation instances that thesystem chooses from as the most optimal group, such as by using agenetic algorithms, as described above. For example, blocks 1404 through1408 can be repeated for different combinations of rule data sets,learned information, risk of inspection, etc. In some embodiments, thereis a tangible output as a result of the consolidation of records, suchthat the goods of the consolidated POs are then removed from a freightvessel and organized for shipment (e.g., loaded into a carrier vehicle)to their appropriate destinations. For example, as a result ofconsolidating records of goods that are not subject to inspection, thegoods may be more quickly loaded for the next phase, such as loading thenon-inspected goods into a delivery truck, as opposed to waiting forinspection to then load the goods. Some embodiments do not consolidaterecords, per se, per block 1408 but rather flag the records as requestedor recommended candidates for consolidation such that the user maychoose to actually consolidate records via user interface selection orinput.

Some embodiments perform additional functionality in response to block1408, such as computing or determining any of the screenshot or userinterface information as described herein with respect to FIG. 6 throughFIG. 13 . For example, some embodiments compute a cost savings based onconsolidating of the plurality of records to the single instance. Thisis described for example, with respect to FIG. 7 through FIG. 12 . Someembodiments cause generation of the user interface 1410 based on thesecomputations or determinations, as described with respect to FIG. 6through FIG. 13 .

Per block 1410, some embodiments cause generation of a user interface.

Some embodiments provide additional functionality subsequent to block1404, such as send a control signal as described with respect to FIG. 5or generate a user interface (e.g., as described with respect to FIGS. 6through 13 ).

FIG. 15 is a schematic diagram of an example visualization of featurespace 1500 that illustrates various feature vectors representingindividual goods (or purchase orders that include a group of goods) andcorresponding risk-of-inspection groups, according to some embodiments.In some embodiments, FIG. 15 represents functionality performed by therisk of inspection component 502 and/or the learning module 510. A“feature vector” (also referred to as a “vector”) as described hereinincludes one or more real numbers, such as a series of floating valuesor integers (e.g., [0, 1, 0, 0]) that represent one or more other realnumbers, a natural language (e.g., English) word and/or other charactersequence or data sets (e.g., a symbol (e.g., @, !, #), a phrase,sentence, contents in a PO, a description of goods, etc.). Such naturallanguage words and/or character sequences correspond to the set offeatures and are encoded or converted into corresponding feature vectorsso that computers can process the corresponding extracted features.

A plurality of feature vectors that are embedded based on distance(e.g., Euclidian distance) represent “feature space.” The distancebetween any two feature vectors or class/cluster of vectors is measuredaccording to any suitable method. For example, in some embodiments,automated cosine (or Euclidian) distance similarity is used to computedistance. Cosine similarity is a measure of similarity between twonon-zero feature vectors of an inner product space that measures thecosine of the angle between the two non-zero feature vectors. In theseembodiments, no similarity is expressed as a 90 degree angle, whiletotal similarity (i.e., the same word) of 1 is a 0 degree angle. Forexample, a 0.98 distance between two contextual vectors reflects a veryhigh similarity while a 0.003 distance reflects little similarity.

Some embodiments generate or derive a feature vector that computers areconfigured to analyze. For example, the word “bananas” on a PO can beconverted into a first feature vector via vector encoding (e.g., one hotencoding). For instance, the word “Banana” may be converted into thevector [1,0,0,0,0,0,0,0,0,0]. This vector representation may correspondto ordered words (e.g., each word in a sentence or vocabulary) andwhether the word is TRUE or present. Because “Banana” is the only wordbeing converted in this example, the integer 1 is used to indicate itsrepresentation. In this example, the PO does not contain any of theother words with it so the other values are represented as 0. In variousembodiments, each character sequence (e.g., a word) in a PO is one-hotencoded by aggregating multiple words of a PO (e.g., bananas, bikes,watch, or other specified goods) into single token. This may beconsidered as one token and is represented as a one hot vector with one1 element and all remaining elements 0s.

In some embodiments, the learning module 510 and/or the risk ofinspection component 502 aggregates each feature value of a vector basedon performing a linear function or otherwise combining the output (e.g.,a dot product or a softmax function) where the output is a featurevector or vector space embedding. The feature vector may thus beindicative of the actual coordinates that a feature vector will beembedded in feature space. For example, using the illustration above,the encoded “banana” feature vector [1,0,0,0,0,0,0,0,0,0] can beconverted or encoded to an output layer vector [1,2], which is the2-dimensional plotting coordinates in feature space.

In some embodiments, the feature space 1500 represents the functionalityused by the risk of inspection component 502 an/or the learning module510 to determine the class or cluster of inspection risk that a givendata point belongs to In some embodiments, the feature space 1500includes classes of data points (e.g., data point 1503-1 and data point1503-2) representing individual feature vectors corresponding tospecific goods, purchase orders that indicate particular goods, and/orparticular entries. These data points are formed together to form aparticular class or cluster. For example, the data point 1503-1 and datapoint 1503-2 have been classified as “high inspection risk” 1103(indicative that the feature values of the data points 1503 are within athreshold distance to or similar to other trained data points). Thereare other classes, such as class 1505 (e.g., “low inspection risk”) andthe class 1107 (e.g., “intermediate inspection risk”).

In an illustrative example of how the feature space 1500 is used,embodiments may receive a set of historical purchase orders and/or goodsthat are labeled as “inspected” or “not inspected” indicative of whetherthe historical purchase orders and/or goods were subject to inspectionor no inspection respectively. Responsively, some embodiments run afirst labeled PO through one or more machine learning models in order toextract and weight features (e.g., date of inspection, date that importwas completed/began, particular goods, time of inspection, port ofinspection, etc.) for the first PO, after which a feature vector (e.g.,representing the data point 1503-1) is embedded in the feature space1500 based on the features. The feature space 1500 in variousembodiments represents a multidimensional coordinate system where eachfeature is associated with one or more dimensions. For example, a firstPO may be embedded where a first axis represents a date of inspectionand the second axis represents the particular goods in the PO. Eachfeature value within the feature vector may be summed or otherwiseaggregated (e.g., via a dot product calculation) to arrive at a finalcoordinate point (e.g., the data point 1503-2) within the feature space1500. Each of the data points within the class 1503, for example, arewithin a feature similarity threshold and so they are close to eachother (e.g., based on Euclidian distance) in the feature space 1500.Responsive to the embedding of the feature vector in the feature space1500, embodiments classify or cluster the first set of entries. Forexample, if a first vector represents data point 1503-1, then theclassification that is nearest to the data point 1503-1 is determined tobe the “high risk of inspection.” Classification 1503 indicative of thePO, good, and/or entry having a high risk of being inspected. In thisway, individual patterns for features can be identified for those goods,POs, and/or entries that have been labeled as “inspected” and “notinspected” so that embodiments can reliably predict the risk ofinspection for a good, PO, and/or entry. For example, a machine learningmodel may identify a pattern that 98% of the goods, POs, and/or entriesthat were inspected were fruits and vegetables. Accordingly, if acurrent PO was being analyzed that includes fruit or vegetables, thenembodiments can predict that a particular entry, PO, and/or good is inthe “high risk of inspection” category or is otherwise at high risk ofbeing inspected.

The machine learning model(s) are able to cluster or classify samples ofnew unseen good, PO, and/or entry (e.g., any PO, good, and/or entryreceived after training). In some embodiments, every good, PO, and/orentry is represented by the median of its samples' embeddings as shownbelow:

C _(J)=Median{f _(Embed)(S _(i) ^(j)): I=1, 2, . . . ,n]

Where f_(embed) is the output of the model, S_(i) ^(j) is the i^(th)sample of the j^(th) class. The prediction for any test sample X isgiven by:

${{Pred}(X)} = {\arg\min\limits_{j}{{{C_{j -}{f_{embed}(X)}}}.}}$

However, it is understood that median is just one way to represent anembedding. Some embodiments alternatively use other statistics likemean, pth percentile, and the like.

Definitions

“And/or” is the inclusive disjunction, also known as the logicaldisjunction and commonly known as the “inclusive or.” For example, thephrase “A, B, and/or C,” means that at least one of A or B or C is true;and “A, B, and/or C” is only false if each of A and B and C is false.

A “set of” items means there exists one or more items; there must existat least one item, but there can also be two, three, or more items. A“subset of” items means there exists one or more items within a groupingof items that contain a common characteristic.

A “plurality of” items means there exists more than one item; there mustexist at least two items, but there can also be three, four, or moreitems.

“Includes” and any variants (e.g., including, include, etc.) means,unless explicitly noted otherwise, “includes, but is not necessarilylimited to.”

A “user” or a “subscriber” includes, but is not necessarily limited to:(i) a single individual human; (ii) an artificial intelligence entitywith sufficient intelligence to act in the place of a single individualhuman or more than one human; (iii) a business entity for which actionsare being taken by a single individual human or more than one human;and/or (iv) a combination of any one or more related “users” or“subscribers” acting as a single “user” or “subscriber.”

The terms “receive,” “provide,” “send,” “input,” “output,” and “report”should not be taken to indicate or imply, unless otherwise explicitlyspecified: (i) any particular degree of directness with respect to therelationship between an object and a subject; and/or (ii) a presence orabsence of a set of intermediate components, intermediate actions,and/or things interposed between an object and a subject.

A “data store” as described herein is any type of repository for storingand/or managing data, whether the data is structured, unstructured, orsemi-structured. For example, a data store can be or include one ormore: databases, files (e.g., of unstructured data), corpuses, digitaldocuments, etc.

A “module” is any set of hardware, firmware, and/or software thatoperatively works to do a function, without regard to whether the moduleis: (i) in a single local proximity; (ii) distributed over a wide area;(iii) in a single proximity within a larger piece of software code; (iv)located within a single piece of software code; (v) located in a singlestorage device, memory, or medium; (vi) mechanically connected; (vii)electrically connected; and/or (viii) connected in data communication. A“sub-module” is a “module” within a “module.”

The terms first (e.g., first request), second (e.g., second request),etc. are not to be construed as denoting or implying order or timesequences unless expressly indicated otherwise. Rather, they are to beconstrued as distinguishing two or more elements. In some embodiments,the two or more elements, although distinguishable, have the samemakeup. For example, a first memory and a second memory may indeed betwo separate memories but they both may be RAM devices that have thesame storage capacity (e.g., 4 GB).

The term “causing” or “cause” means that one or more systems (e.g.,computing devices) and/or components (e.g., processors) may in inisolation or in combination with other systems and/or components bringabout or help bring about a particular result or effect. For example,the logistics server(s) 105 may “cause” a message to be displayed to acomputing entity 110 (e.g., via transmitting a message to the userdevice) and/or the same computing entity 110 may “cause” the samemessage to be displayed (e.g., via a processor that executesinstructions and data in a display memory of the user device).Accordingly, one or both systems may in isolation or together “cause”the effect of displaying a message.

The term “real time” includes any time frame of sufficiently shortduration as to provide reasonable response time for informationprocessing as described. Additionally, the term “real time” includeswhat is commonly termed “near real time,” generally any time frame ofsufficiently short duration as to provide reasonable response time foron-demand information processing as described (e.g., within a portion ofa second or within a few seconds). These terms, while difficult toprecisely define, are well understood by those skilled in the art.

The term “machine learning model” refers to a model that is used formachine learning tasks or operations. A machine learning model cancomprise a title and encompass one or more input images or data, targetvariables, layers, classifiers, etc. In various embodiments, a machinelearning model can receive an input (e.g., risk of inspection), andbased on the input identify patterns or associations in order to predicta given output (e.g., that a certain set of entries should beconsolidate). Machine learning models can be or include any suitablemodel, such as one or more: neural networks, word2Vec models, Bayesiannetworks, Random Forests, Boosted Trees, etc. “Machine learning” asdescribed herein, in particular embodiments, corresponds to algorithmsthat parse or extract features of historical data (e.g., a data store ofhistorical images), learn (e.g., via training) about the historical databy making observations or identifying patterns in data, and then receivea subsequent input (e.g., a current image) in order to make adetermination, prediction, and/or classification of the subsequent inputbased on the learning without relying on rules-based programming (e.g.,conditional statement rules).

V. CONCLUSION

Many modifications and other embodiments of the inventions set forthherein will come to mind to one skilled in the art to which theseinventions pertain having the benefit of the teachings presented in theforegoing description and the associated drawings. Therefore, it is tobe understood that the inventions are not to be limited to the specificembodiments disclosed and that modifications and other embodiments areintended to be included within the scope of the appended claims.Although specific terms are employed herein, they are used in a genericand descriptive sense only and not for purposes of limitation, unlessdescribed otherwise.

What is claimed is:
 1. A computer-implemented method comprising:receiving rule data sets associated with constraints controlling how aplurality of purchase orders are to be consolidated to an entry, eachpurchase order, of the plurality of purchases orders, include one ormore attributes of one or more goods that are to be received forimporting, the entry corresponding to a customs clearance documentidentifying at least one good that is to be imported; based at least inpart on the rule data sets, automatically generating a score indicativeof an estimated risk for inspection by a customs clearance agent for afirst set of goods that are to be imported, the first set of goods beingassociated with the plurality of purchase orders; based at least in parton the generated score, automatically consolidating the plurality ofpurchase orders to a single entry for importing the first set of goods;in response to the automatic consolidation of the plurality of purchaseorders, storing the single entry to a storage device such that theplurality of purchase orders are configured to be accessed from thestorage device via a single computer input/output (I/O) operation; andbased at least in part on the consolidating, causing a user interface tobe generated that renders information associated with the consolidating.2. The method of claim 1, wherein the rule data sets include one or morerules from a customs agency.
 3. The method of claim 2, wherein the ruledata sets include a predefined rule that the customs agency enforces andare to be abided by, wherein the one or more rules includes at least oneof: a first requirement that a specific good is always inspectedregardless of where it came from, and a second rule that a good is onlyto inspected if it came from a specific geographic location associatedwith pestilence or disease.
 4. The method of claim 1, wherein the ruledata sets include one or more rules from a buyer associated with theplurality of purchase orders.
 5. The method of claim 4, wherein the oneor more rules includes a predefined rule that the seller or the buyer ofimported goods enforces to be abided by, and wherein the one or morerules include at least one of: a first rule that indicates that certaingoods are not to be contained in a same entry for importing, a secondrule that certain goods are always to be contained in the same entry forimporting.
 6. The method of claim 1, wherein the generating of the scoreis further based on historical purchase orders that have been labeled asinspected or not inspected.
 7. The method of claim 1, further comprisingcomputing a cost savings based on the consolidating of the plurality ofpurchase orders to the single entry, wherein the cost savings isincluded in the user interface.
 8. The method of claim 1, wherein theconsolidating is further based at least in part on sensor data obtainfrom a freight vessel.
 9. One or more computer storage media havingcomputer-executable instructions embodied thereon that, when executed,by one or more processors, cause the one or more processors to perform amethod, the method comprising: receiving at least one rule data setassociated with constraints controlling how a plurality of purchaseorders are to be consolidated to an entry, each purchase order, of theplurality of purchases orders, includes one or more attributes of one ormore goods that are to be received for importing, the entrycorresponding to a customs clearance document identifying at least onegood that is to be imported; based at least in part on the rule dataset, automatically generating, for each good, of a first set of goods, ascore indicative of an estimated risk for inspection by a customsclearance agent for the good that is to be imported, the first set ofgoods being associated with the plurality of purchase orders; based atleast in part on the generated score for each good, automaticallyconsolidating the plurality of purchase orders to a single entry forimporting the first set of goods; in response to the automaticconsolidation of the plurality of purchase orders, storing the singleentry to a storage device; and based at least in part on theconsolidating, causing a user interface to be generated that rendersinformation associated with the consolidating.
 10. The one or morecomputer storage media of claim 9, wherein the rule data set includesone or more rules from a customs agency.
 11. The one or more computerstorage media of claim 9, wherein the rule data set includes one or morerules from a buyer or seller associated with the plurality of purchaseorders.
 12. The one or more computer storage media of claim 9, whereinthe generated score is further based on predicting, via a machinelearning model, that a respective good is likely to be inspected. 13.The one or more computer storage media of claim 9, the method furthercomprising computing a cost savings based on the consolidating of theplurality of purchase orders to the single entry, wherein the costsavings is included in the user interface.
 14. The one or more computerstorage media of claim 9, wherein the consolidating is further based atleast in part on sensor data obtain from a freight vessel.
 15. A systemfor implementing classification-based adjustable seek energy settings instorage device systems, the system comprising: one or more processors;and one or more computer storage media storing computer-useableinstructions that, when used by the one or more processors, cause theone or more processors to perform a method, the method comprising:receiving at least one rule data set associated with constraintscontrolling how a plurality of purchase orders are to be consolidated toan entry, each purchase order, of the plurality of purchases orders,includes one or more attributes of one or more goods that are to bereceived for importing, the entry corresponding to a customs clearancedocument identifying at least one good that is to be imported; based atleast in part on the rule data set, generating, for each good, of afirst set of goods, a score indicative of an estimated risk forinspection by a customs clearance agent for the good that is to beimported, the first set of goods being associated with the plurality ofpurchase orders; based at least in part on the generated score for eachgood, automatically consolidating the plurality of purchase orders to asingle entry for importing the first set of goods; storing the singleentry to a storage device; and based at least in part on theconsolidating, causing a user interface to be generated that rendersinformation associated with the consolidating.
 16. The system of claim15, wherein the rule data set includes one or more rules from a buyer orseller associated with the plurality of purchase orders.
 17. The systemof claim 16, wherein the one or more rules includes a predefined rulethat the seller or the buyer of imported goods enforces to be abided by,and wherein the one or more rules include at least one of: a first rulethat indicates that certain goods are not to be contained in a sameentry for importing, a second rule that certain goods are always to becontained in the same entry for importing.
 18. The system of claim 15,wherein the rule data set includes one or more rules from a customsagency.
 19. The system of claim 18, wherein the rule data set includes apredefined rule that the customs agency enforces and are to be abidedby, wherein the one or more rules includes at least one of: a firstrequirement that a specific good is always inspected regardless of whereit came from, and a second rule that a good is only to inspected if itcame from a specific geographic location associated with pestilence ordisease.
 20. The system of claim 15, the method further comprisingcomputing a cost savings based on the consolidating of the plurality ofpurchase orders to the single entry, wherein the cost savings isincluded in the user interface.